Language Not Auditory Experience is Related to Parent-Reported Executive Functioning in Preschool-Aged Deaf and Hard of Hearing Children

Load Packages

Load the following libraries. If they are not installed, run install.packages (“packagename”)

#from https://www.vikram-baliga.com/blog/2015/7/19/a-hassle-free-way-to-verify-that-r-packages-are-installed-and-loaded
packages = c("tidyverse","ggplot2","beeswarm","colorspace", "stargazer", "qwraps2", "gridExtra","ggpubr","car","olsrr","ggbeeswarm", "broom", "rmarkdown", "table1", "kableExtra", "epitools", "FSA", "Hmisc", "userfriendlyscience")
package.check <- lapply(packages, FUN = function(x) {
  if (!require(x, character.only = TRUE)) {
    install.packages(x, dependencies = TRUE)
    library(x, character.only = TRUE)
  }
})

#verify they are loaded
search()

Setup Dataframes

First, import the data from the data (CSV) file

setwd("/Users/emilycarrigan/Dropbox/Data Analysis Work/") #This should be wherever your file is saved
BRIEF <- read.csv("BRIEF_SLaM&CGdata_CG_200423.csv", na.strings = c("N/A", "", "Unknown", "Excluded"))

Then, subset imported dataframe to remove participants missing age or SES information

• This results in 139 participants

Add 4 columns:

• Language Modality
• Language Timing
• 4-group split
• 3-way group split

BRIEF <- subset(BRIEF, BRIEF$AgeMonths < 100 & BRIEF$AgeMonths!="" & BRIEF$SES..3.66.!="")

BRIEF$Language_Modality <- factor(ifelse(BRIEF$Group_4cat == "English Early" | BRIEF$Group_4cat == "English Later", "English", "ASL"), levels = c("English", "ASL"))
BRIEF$Language_Timing <- factor(as.character(BRIEF$Group_2cat), levels = c("Early", "Later"), exclude="")

BRIEF$LanguageGroup <- as.factor(factor(as.character(BRIEF$Group_4cat), levels = c("English Early", "ASL Early", "English Later", "ASL Later"), labels = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"), exclude=NA))

BRIEF$Hearing_Level_3_Cat <- as.factor(factor(as.character(BRIEF$Hearing_Level_3_Cat), levels = c("TH", "deaf", "HoH"), labels = c("Typically Hearing", "Deaf", "Hard of Hearing"), exclude=NA))

BRIEF$Hearing.Device <- dplyr::recode(BRIEF$Hearing.Device, "BAHA" = "Other")
BRIEF$Hearing.Device <- as.factor(factor(as.character(BRIEF$Hearing.Device), levels = c("HA", "CI", "HA & CI", "Other", "None"), labels = c("Hearing Aid", "Cochlear Implant", "Hearing Aid & Cochlear Implant", "Other", "None"), exclude=NA))

#Recode Race to have fewer categories
BRIEF$Race_recoded <- dplyr::recode(as.character(BRIEF$Race), 'Asian' = "Asian", 'Black or African American'="Black or African American", 'More than One'="More than one", 'Other'="Other/Missing", 'Unsure, or prefer not to answer' = "Other/Missing", 'White'="White", .missing="Other/Missing")

#update ORDER of groups
BRIEF$Race_recoded <- as.factor(factor(as.character(BRIEF$Race_recoded), levels = c("Asian", "Black or African American", "White", "More than one", "Other/Missing"), exclude=NA))

#Recode Ethnicity to have fewer categories
BRIEF$Ethnicity_recoded <- dplyr::recode(as.character(BRIEF$Ethnicity), 'Hispanic' = "Hispanic", 'NonHispanic'="Non-Hispanic", 'PreferNotToAnswer'="Prefer not to answer", .missing="Missing")

#update ORDER of groups
BRIEF$Ethnicity_recoded <- as.factor(factor(as.character(BRIEF$Ethnicity_recoded), levels = c("Hispanic", "Non-Hispanic", "Prefer not to answer", "Missing"), exclude=NA))

Create dataframe for participants with Age of Auditory Exposure and Age of Language Exposure

BRIEF <- dplyr::mutate(BRIEF, AoAE = ifelse(BRIEF$Age.of.Auditory.Exposure..mo.=="None", as.character(BRIEF$AgeMonths), as.character(BRIEF$Age.of.Auditory.Exposure..mo.)))

BRIEF$AoAE <- as.integer(BRIEF$AoAE)

BRIEF_AgeOf <- subset(BRIEF, BRIEF$AoAE!='' & BRIEF$Age.of.Language.Exposure..mo.!='')

• This is the dataframe used for linear models in Question 2
1. Create numerical “AoAE” variable from Age of Auditory Exposure variable, converting “None” to current age
2. Change structure of Age of Auditory Exposure from character to integer
3. Remove participants for whom either of these two variables is missing
4. This results in 119 participants

Create dataframe with participants who are age appropriate for the BRIEF-P (less than 5 years 11 Months old)

• 102 participants

BRIEF_AgeApp <- subset(BRIEF_AgeOf, BRIEF_AgeOf$AgeMonths <= 71)

---

Demographic Information

Table 1.

table1::label(BRIEF$AgeMonths) <- "Age (Months)"
table1::label(BRIEF$SES..3.66.) <- "SES"
table1::label(BRIEF$Sex) <- "Sex"
table1::label(BRIEF$Race_recoded) <- "Race"
table1::label(BRIEF$Ethnicity_recoded) <- "Ethnicity"

 
table1::table1(~AgeMonths + SES..3.66. + Sex + Race_recoded + Ethnicity_recoded  | LanguageGroup, data = BRIEF, overall=F)

	Typically Hearing (N=46)	Early ASL (N=28)	Later English (N=35)	Later ASL (N=30)
Age (Months)
Mean (SD)	54.7 (10.9)	61.2 (14.2)	58.5 (11.8)	66.7 (15.1)
Median [Min, Max]	54.5 [37.0, 85.0]	58.0 [41.0, 91.0]	60.0 [37.0, 78.0]	71.0 [37.0, 90.0]
SES
Mean (SD)	55.6 (8.83)	48.8 (16.3)	47.7 (15.8)	43.7 (17.2)
Median [Min, Max]	56.0 [21.5, 66.0]	56.5 [11.0, 66.0]	53.0 [3.00, 66.0]	49.8 [9.00, 62.0]
Sex
Female	24 (52.2%)	18 (64.3%)	20 (57.1%)	13 (43.3%)
Male	22 (47.8%)	10 (35.7%)	15 (42.9%)	17 (56.7%)
Race
Asian	0 (0%)	0 (0%)	1 (2.9%)	4 (13.3%)
Black or African American	0 (0%)	0 (0%)	2 (5.7%)	0 (0%)
White	43 (93.5%)	25 (89.3%)	24 (68.6%)	20 (66.7%)
More than one	3 (6.5%)	1 (3.6%)	4 (11.4%)	3 (10.0%)
Other/Missing	0 (0%)	2 (7.1%)	4 (11.4%)	3 (10.0%)
Ethnicity
Hispanic	3 (6.5%)	0 (0%)	5 (14.3%)	6 (20.0%)
Non-Hispanic	42 (91.3%)	18 (64.3%)	26 (74.3%)	21 (70.0%)
Prefer not to answer	0 (0%)	1 (3.6%)	0 (0%)	0 (0%)
Missing	1 (2.2%)	9 (32.1%)	4 (11.4%)	3 (10.0%)

Comparing SES across Language Groups

ggboxplot(BRIEF, x = "LanguageGroup", y = "SES..3.66.", 
          color = "LanguageGroup",
          order = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"),
          ylab = "SES", xlab = "Language Group")

ggplot(data=BRIEF, mapping=aes(x=SES..3.66.))+ geom_histogram(binwidth=10) + facet_grid(~LanguageGroup)

leveneTest(SES..3.66.~LanguageGroup, data=BRIEF)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value  Pr(>F)  
## group   3  2.7556 0.04493 *
##       135                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

SES_LangGrp <- kruskal.test(SES..3.66.~LanguageGroup, data=BRIEF)
SES_LangGrp 

## 
##  Kruskal-Wallis rank sum test
## 
## data:  SES..3.66. by LanguageGroup
## Kruskal-Wallis chi-squared = 11.094, df = 3, p-value = 0.01123

dunnTest(SES..3.66.~LanguageGroup, data=BRIEF, method="hochberg")

## Dunn (1964) Kruskal-Wallis multiple comparison

##   p-values adjusted with the Hochberg method.

##                          Comparison          Z     P.unadj      P.adj
## 1             Early ASL - Later ASL  1.6920471 0.090637003 0.36254801
## 2         Early ASL - Later English  0.9135500 0.360953336 0.72190667
## 3         Later ASL - Later English -0.8560483 0.391971056 0.39197106
## 4     Early ASL - Typically Hearing -1.1954137 0.231925419 0.69577626
## 5     Later ASL - Typically Hearing -3.1155898 0.001835775 0.01101465
## 6 Later English - Typically Hearing -2.3101242 0.020881279 0.10440639

#selected hochberg correction method referencing this site: https://towardsdatascience.com/an-overview-of-methods-to-address-the-multiple-comparison-problem-310427b3ba92

Findings:

• Later ASL has significantly lower SES than Typically Hearing
• No other between-group differences in SES

Comparing Age across Language Groups

ggboxplot(BRIEF, x = "LanguageGroup", y = "AgeMonths", 
          color = "LanguageGroup",
          order = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"),
          ylab = "Age (Months)", xlab = "Language Group")

ggplot(data=BRIEF, mapping=aes(x=AgeMonths))+ geom_histogram(binwidth=6) + facet_grid(~LanguageGroup)

leveneTest(AgeMonths~LanguageGroup, data=BRIEF)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   3  1.3757 0.2529
##       135

Age_LangGrp <- kruskal.test(AgeMonths~LanguageGroup, data=BRIEF)
Age_LangGrp 

## 
##  Kruskal-Wallis rank sum test
## 
## data:  AgeMonths by LanguageGroup
## Kruskal-Wallis chi-squared = 13.74, df = 3, p-value = 0.003281

dunnTest(AgeMonths~LanguageGroup, data=BRIEF, method="hochberg")

## Dunn (1964) Kruskal-Wallis multiple comparison

##   p-values adjusted with the Hochberg method.

##                          Comparison          Z      P.unadj       P.adj
## 1             Early ASL - Later ASL -1.5793242 0.1142617107 0.342785132
## 2         Early ASL - Later English  0.4360839 0.6627758697 0.662775870
## 3         Later ASL - Later English  2.1123433 0.0346570179 0.173285089
## 4     Early ASL - Typically Hearing  1.8632966 0.0624205521 0.249682208
## 5     Later ASL - Typically Hearing  3.6715398 0.0002410935 0.001446561
## 6 Later English - Typically Hearing  1.4982350 0.1340721937 0.268144387

Findings:

• Later ASL group significantly older than Typically Hearing group
• No other between-group differences in Age

Table 2.

table1::label(BRIEF$Hearing_Level_3_Cat) <- "Pre-device hearing level (3 Category)"
table1::label(BRIEF$Hearing.Device) <- "Type of Hearing Device"
table1::label(BRIEF$AoAE) <- "Age of First Auditory Exposure (Months)"
table1::label(BRIEF$Age.of.Language.Exposure..mo.) <- "Age of First Language Exposure (Months)"

table1::table1(~Hearing_Level_3_Cat + Hearing.Device + AoAE + Age.of.Language.Exposure..mo.| LanguageGroup, data = BRIEF, overall=F)

	Typically Hearing (N=46)	Early ASL (N=28)	Later English (N=35)	Later ASL (N=30)
Pre-device hearing level (3 Category)
Typically Hearing	46 (100%)	0 (0%)	0 (0%)	0 (0%)
Deaf	0 (0%)	15 (53.6%)	13 (37.1%)	21 (70.0%)
Hard of Hearing	0 (0%)	9 (32.1%)	21 (60.0%)	7 (23.3%)
Missing	0 (0%)	4 (14.3%)	1 (2.9%)	2 (6.7%)
Type of Hearing Device
Hearing Aid	0 (0%)	12 (42.9%)	23 (65.7%)	13 (43.3%)
Cochlear Implant	0 (0%)	0 (0%)	9 (25.7%)	8 (26.7%)
Hearing Aid & Cochlear Implant	0 (0%)	0 (0%)	2 (5.7%)	1 (3.3%)
Other	0 (0%)	1 (3.6%)	1 (2.9%)	0 (0%)
None	46 (100%)	15 (53.6%)	0 (0%)	8 (26.7%)
Age of First Auditory Exposure (Months)
Mean (SD)	0 (0)	44.1 (27.9)	16.1 (17.3)	35.5 (27.5)
Median [Min, Max]	0 [0, 0]	46.5 [0, 91.0]	12.0 [0, 58.0]	34.0 [0, 90.0]
Missing	0 (0%)	8 (28.6%)	0 (0%)	5 (16.7%)
Age of First Language Exposure (Months)
Mean (SD)	0 (0)	0 (0)	16.2 (17.3)	41.0 (13.5)
Median [Min, Max]	0 [0, 0]	0 [0, 0]	12.0 [0, 58.0]	36.5 [18.0, 76.0]
Missing	0 (0%)	0 (0%)	0 (0%)	2 (6.7%)

Comparing Age of Auditory Exposure across Language Groups

ggboxplot(BRIEF, x = "LanguageGroup", y = "AoAE", 
          color = "LanguageGroup",
          order = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"),
          ylab = "Age of Auditory Exposure (Months)", xlab = "Language Group")

## Warning: Removed 13 rows containing non-finite values (stat_boxplot).

ggplot(data=BRIEF, mapping=aes(x=AoAE))+ geom_histogram(binwidth=15) + facet_grid(~LanguageGroup)

## Warning: Removed 13 rows containing non-finite values (stat_bin).

leveneTest(AoAE~LanguageGroup, data=BRIEF)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value    Pr(>F)    
## group   3   29.86 1.513e-14 ***
##       122                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

AoAE_LangGrp <- kruskal.test(AoAE~LanguageGroup, data=BRIEF)
AoAE_LangGrp 

## 
##  Kruskal-Wallis rank sum test
## 
## data:  AoAE by LanguageGroup
## Kruskal-Wallis chi-squared = 75.907, df = 3, p-value = 2.316e-16

dunnTest(AoAE~LanguageGroup, data=BRIEF, method="hochberg")

## Warning: Some rows deleted from 'x' and 'g' because missing data.

## Dunn (1964) Kruskal-Wallis multiple comparison

##   p-values adjusted with the Hochberg method.

##                          Comparison         Z      P.unadj        P.adj
## 1             Early ASL - Later ASL 0.6564261 5.115500e-01 5.115500e-01
## 2         Early ASL - Later English 3.0235127 2.498585e-03 7.495756e-03
## 3         Later ASL - Later English 2.4844464 1.297531e-02 2.595061e-02
## 4     Early ASL - Typically Hearing 7.1732046 7.326221e-13 4.395733e-12
## 5     Later ASL - Typically Hearing 6.9397910 3.926804e-12 1.963402e-11
## 6 Later English - Typically Hearing 4.7872196 1.691078e-06 6.764310e-06

Findings:

• Typically hearing group has significantly earlier age of auditory exposure than all three DHH groups (as expected)
• Later English group has significantly earlier age of auditory exposure than both Early and Later ASL groups
• Early and Later ASL groups did not differ in terms of their age of auditory exposure

Comparing Age of Language Exposure across Language Groups

ggboxplot(BRIEF, x = "LanguageGroup", y = "Age.of.Language.Exposure..mo.", 
          color = "LanguageGroup",
          order = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"),
          ylab = "Age of Language Exposure (Months)", xlab = "Language Group")

## Warning: Removed 2 rows containing non-finite values (stat_boxplot).

ggplot(data=BRIEF, mapping=aes(x=Age.of.Language.Exposure..mo.))+ geom_histogram(binwidth=10) + facet_grid(~LanguageGroup)

## Warning: Removed 2 rows containing non-finite values (stat_bin).

leveneTest(Age.of.Language.Exposure..mo.~LanguageGroup, data=BRIEF)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value    Pr(>F)    
## group   3  34.088 < 2.2e-16 ***
##       133                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

AoLE_LangGrp <- kruskal.test(Age.of.Language.Exposure..mo.~LanguageGroup, data=BRIEF)
AoLE_LangGrp 

## 
##  Kruskal-Wallis rank sum test
## 
## data:  Age.of.Language.Exposure..mo. by LanguageGroup
## Kruskal-Wallis chi-squared = 107.38, df = 3, p-value < 2.2e-16

dunnTest(Age.of.Language.Exposure..mo.~LanguageGroup, data=BRIEF, method="hochberg")

## Warning: Some rows deleted from 'x' and 'g' because missing data.

## Dunn (1964) Kruskal-Wallis multiple comparison

##   p-values adjusted with the Hochberg method.

##                          Comparison         Z      P.unadj        P.adj
## 1             Early ASL - Later ASL -8.173761 2.989226e-16 1.494613e-15
## 2         Early ASL - Later English -4.861165 1.166972e-06 3.500916e-06
## 3         Later ASL - Later English  3.754736 1.735245e-04 3.470490e-04
## 4     Early ASL - Typically Hearing  0.000000 1.000000e+00 1.000000e+00
## 5     Later ASL - Typically Hearing  9.113810 7.953855e-20 4.772313e-19
## 6 Later English - Typically Hearing  5.495004 3.907022e-08 1.562809e-07

Findings:

• Both Early groups significantly different from both Later groups (as expected)
• Later ASL group has significantly later age of language exposure than Later English groups

---

Results

Question 1: Do DHH children with access to sign language but not auditory input from birth exhibit executive functioning skills comparable to their typically-hearing peers?

Approach: Compare (via Welch’s t-tests) the BRIEF raw scores of Early English group to those of Early ASL group

Create dataframe with only children exposed to language early and perform t-tests for all BRIEF-P scales

BRIEF_early <- subset(BRIEF_AgeOf, BRIEF_AgeOf$Language_Timing=="Early")

Table 3.

BRIEF Scores & Welch two sample t-tests for two “Early” groups

table1::label(BRIEF_early$GEC_RawScore) <- "Global Executive Composite"
table1::label(BRIEF_early$Inhibit_RawScore) <- "Inhibition"
table1::label(BRIEF_early$Shift_RawScore) <- "Shift"
table1::label(BRIEF_early$Emotional.Control_RawScore) <- "Emotional Control"
table1::label(BRIEF_early$Working.Memory_RawScore) <- "Working Memory"
table1::label(BRIEF_early$Plan.Organize_RawScore) <- "Plan/Organize"

table1(~GEC_RawScore + Inhibit_RawScore + Shift_RawScore + Emotional.Control_RawScore + Working.Memory_RawScore + Plan.Organize_RawScore | Language_Modality, data = BRIEF_early, overall=F)

	English (N=46)	ASL (N=20)
Global Executive Composite
Mean (SD)	87.8 (18.7)	85.6 (13.5)
Median [Min, Max]	87.5 [63.0, 155]	87.5 [63.0, 103]
Inhibition
Mean (SD)	22.8 (5.93)	22.9 (5.26)
Median [Min, Max]	23.0 [16.0, 46.0]	22.0 [16.0, 34.0]
Shift
Mean (SD)	13.0 (3.11)	13.0 (2.67)
Median [Min, Max]	12.0 [10.0, 24.0]	12.5 [10.0, 17.0]
Emotional Control
Mean (SD)	15.0 (3.67)	14.5 (2.78)
Median [Min, Max]	14.0 [10.0, 24.0]	14.0 [10.0, 19.0]
Working Memory
Mean (SD)	22.7 (6.61)	22.0 (4.50)
Median [Min, Max]	20.5 [17.0, 48.0]	21.0 [17.0, 31.0]
Plan/Organize
Mean (SD)	14.3 (3.40)	13.4 (2.52)
Median [Min, Max]	13.5 [10.0, 23.0]	13.0 [10.0, 18.0]

t.test(BRIEF_early$GEC_RawScore~BRIEF_early$Language_Modality)

## 
##  Welch Two Sample t-test
## 
## data:  BRIEF_early$GEC_RawScore by BRIEF_early$Language_Modality
## t = 0.53481, df = 49.488, p-value = 0.5952
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -6.016656 10.381874
## sample estimates:
## mean in group English     mean in group ASL 
##              87.78261              85.60000

t.test(BRIEF_early$Inhibit_RawScore~BRIEF_early$Language_Modality)

## 
##  Welch Two Sample t-test
## 
## data:  BRIEF_early$Inhibit_RawScore by BRIEF_early$Language_Modality
## t = -0.060772, df = 40.562, p-value = 0.9518
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -3.052056  2.873796
## sample estimates:
## mean in group English     mean in group ASL 
##              22.76087              22.85000

t.test(BRIEF_early$Shift_RawScore~BRIEF_early$Language_Modality)

## 
##  Welch Two Sample t-test
## 
## data:  BRIEF_early$Shift_RawScore by BRIEF_early$Language_Modality
## t = 0.095418, df = 41.938, p-value = 0.9244
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -1.445606  1.589084
## sample estimates:
## mean in group English     mean in group ASL 
##              13.02174              12.95000

t.test(BRIEF_early$Emotional.Control_RawScore~BRIEF_early$Language_Modality)

## 
##  Welch Two Sample t-test
## 
## data:  BRIEF_early$Emotional.Control_RawScore by BRIEF_early$Language_Modality
## t = 0.64064, df = 47.264, p-value = 0.5249
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -1.130341  2.186863
## sample estimates:
## mean in group English     mean in group ASL 
##              14.97826              14.45000

t.test(BRIEF_early$Working.Memory_RawScore~BRIEF_early$Language_Modality)

## 
##  Welch Two Sample t-test
## 
## data:  BRIEF_early$Working.Memory_RawScore by BRIEF_early$Language_Modality
## t = 0.51255, df = 52.046, p-value = 0.6104
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -2.091134  3.525916
## sample estimates:
## mean in group English     mean in group ASL 
##              22.71739              22.00000

t.test(BRIEF_early$Plan.Organize_RawScore~BRIEF_early$Language_Modality)

## 
##  Welch Two Sample t-test
## 
## data:  BRIEF_early$Plan.Organize_RawScore by BRIEF_early$Language_Modality
## t = 1.2659, df = 48.223, p-value = 0.2116
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.5612263  2.4699219
## sample estimates:
## mean in group English     mean in group ASL 
##              14.30435              13.35000

Findings: Early ASL group did not significantly differ from Typically Hearing group on any subscale ***

Question 2: Do age of access to auditory input and age of language exposure significantly predict executive functioning?

Approach: Linear regression models for the BRIEF-P raw scores on general composite score and each subscale predicted by age of auditory and age of language exposure, and other demographic characteristics (SES, sex, age)

Also testing whether other demographic variables or ways of characterizing group differences (Length of Auditory Exposure, Language Modality) are better predictive of BRIEF scores

Checking correlations between continuous (numerical) predictors

## Function below from: http://www.sthda.com/english/wiki/correlation-matrix-formatting-and-visualization
num_predictors <- BRIEF_AgeOf[, c("SES..3.66.", "AgeMonths", "Age.of.Language.Exposure..mo.", "AoAE")]
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    row = rownames(cormat)[row(cormat)[ut]],
    column = rownames(cormat)[col(cormat)[ut]],
    cor  =(cormat)[ut],
    p = pmat[ut]
    )
}
BRIEF_num_pred <- rcorr(as.matrix(num_predictors), type = c("pearson"))
flattenCorrMatrix(BRIEF_num_pred$r, BRIEF_num_pred$P)

##                             row                        column          cor
## 1                    SES..3.66.                     AgeMonths  0.006453835
## 2                    SES..3.66. Age.of.Language.Exposure..mo. -0.165108899
## 3                     AgeMonths Age.of.Language.Exposure..mo.  0.397791343
## 4                    SES..3.66.                          AoAE -0.152264510
## 5                     AgeMonths                          AoAE  0.400178811
## 6 Age.of.Language.Exposure..mo.                          AoAE  0.452745947
##              p
## 1 9.430540e-01
## 2 6.575670e-02
## 3 4.352450e-06
## 4 9.004399e-02
## 5 3.765614e-06
## 6 1.150699e-07

Findings:

• SES is not significantly correlated with any other continuous predictors
• Age correlated with both Age of Language Exposure and Age of Auditory Exposure (unsurprising)
• Age of Language exposure significantly correlated with Age of Auditory Exposure (also unsurprising given that this will be correlated for participants learning spoken English, which is >50% of our sample)

Global Executive Composite Models

Step 1. Base Model (Demographic variables only):

overall_all_base <- lm(data=BRIEF_AgeOf, formula = GEC_RawScore~SES..3.66.+Sex+AgeMonths)
summary(overall_all_base)

## 
## Call:
## lm(formula = GEC_RawScore ~ SES..3.66. + Sex + AgeMonths, data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -49.301 -16.784  -0.489  10.601  60.607 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  96.5244    11.0764   8.714 1.83e-14 ***
## SES..3.66.   -0.3029     0.1283  -2.360   0.0199 *  
## SexMale      -0.6580     3.6146  -0.182   0.8559    
## AgeMonths     0.1772     0.1393   1.272   0.2057    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 19.83 on 121 degrees of freedom
## Multiple R-squared:  0.05657,    Adjusted R-squared:  0.03318 
## F-statistic: 2.419 on 3 and 121 DF,  p-value: 0.06954

AIC(overall_all_base)

## [1] 1107.494

Step 2. Add Age of Language Exposure:

overall_all_AoLE <- lm(data=BRIEF_AgeOf, formula = GEC_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.)
summary(overall_all_AoLE)

## 
## Call:
## lm(formula = GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo., 
##     data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -53.541 -14.523  -1.440   9.773  66.360 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   101.05856   10.78623   9.369 5.47e-16 ***
## SES..3.66.                     -0.23515    0.12571  -1.871  0.06384 .  
## SexMale                        -1.91594    3.51118  -0.546  0.58631    
## AgeMonths                      -0.01617    0.14781  -0.109  0.91308    
## Age.of.Language.Exposure..mo.   0.32768    0.10407   3.149  0.00207 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 19.14 on 120 degrees of freedom
## Multiple R-squared:  0.1286, Adjusted R-squared:  0.09952 
## F-statistic: 4.426 on 4 and 120 DF,  p-value: 0.002261

AIC(overall_all_AoLE)

## [1] 1099.571

Compare Model from Step 2 to model from Step 1:

anova(overall_all_base, overall_all_AoLE)

## Analysis of Variance Table
## 
## Model 1: GEC_RawScore ~ SES..3.66. + Sex + AgeMonths
## Model 2: GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
##   Res.Df   RSS Df Sum of Sq      F  Pr(>F)   
## 1    121 47591                               
## 2    120 43959  1    3631.8 9.9142 0.00207 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

3a. Add Age of Auditory Exposure:

overall_all_AoLE_AoAE <- lm(data=BRIEF_AgeOf, formula = GEC_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+AoAE)
summary(overall_all_AoLE_AoAE) 

## 
## Call:
## lm(formula = GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. + 
##     AoAE, data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -54.475 -13.271   0.398  10.333  65.083 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   100.35055   10.83174   9.264 1.03e-15 ***
## SES..3.66.                     -0.24696    0.12664  -1.950  0.05351 .  
## SexMale                        -1.80034    3.51807  -0.512  0.60978    
## AgeMonths                       0.02029    0.15417   0.132  0.89550    
## Age.of.Language.Exposure..mo.   0.35769    0.11010   3.249  0.00151 ** 
## AoAE                           -0.06959    0.08247  -0.844  0.40045    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 19.16 on 119 degrees of freedom
## Multiple R-squared:  0.1338, Adjusted R-squared:  0.09735 
## F-statistic: 3.675 on 5 and 119 DF,  p-value: 0.003968

AIC(overall_all_AoLE_AoAE)

## [1] 1100.826

3b. Add Length of Auditory Experience:

overall_all_AoLE_LoAE <- lm(data=BRIEF_AgeOf, formula = GEC_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+Length.of.Auditory.Exposure)
summary(overall_all_AoLE_LoAE)

## 
## Call:
## lm(formula = GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. + 
##     Length.of.Auditory.Exposure, data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -54.333 -13.081   0.011  10.348  65.447 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   100.55746   10.83983   9.277 9.67e-16 ***
## SES..3.66.                     -0.24285    0.12657  -1.919  0.05742 .  
## SexMale                        -1.79263    3.52479  -0.509  0.61199    
## AgeMonths                      -0.04401    0.15425  -0.285  0.77589    
## Age.of.Language.Exposure..mo.   0.35299    0.11136   3.170  0.00194 ** 
## Length.of.Auditory.Exposure     0.05363    0.08260   0.649  0.51739    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 19.19 on 119 degrees of freedom
## Multiple R-squared:  0.1316, Adjusted R-squared:  0.09516 
## F-statistic: 3.608 on 5 and 119 DF,  p-value: 0.004489

AIC(overall_all_AoLE_LoAE)

## [1] 1101.129

3c. Add Modality:

overall_all_AoLE_Modality <- lm(data=BRIEF_AgeOf, formula = GEC_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+Language_Modality)
summary(overall_all_AoLE_Modality)

## 
## Call:
## lm(formula = GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. + 
##     Language_Modality, data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -51.021 -13.485  -0.447  10.692  65.208 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    1.015e+02  1.080e+01   9.399 4.97e-16 ***
## SES..3.66.                    -2.458e-01  1.263e-01  -1.947  0.05392 .  
## SexMale                       -1.885e+00  3.513e+00  -0.537  0.59259    
## AgeMonths                      9.928e-04  1.490e-01   0.007  0.99469    
## Age.of.Language.Exposure..mo.  3.582e-01  1.089e-01   3.289  0.00132 ** 
## Language_ModalityASL          -3.731e+00  3.915e+00  -0.953  0.34253    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 19.15 on 119 degrees of freedom
## Multiple R-squared:  0.1352, Adjusted R-squared:  0.09883 
## F-statistic:  3.72 on 5 and 119 DF,  p-value: 0.00365

AIC(overall_all_AoLE_Modality)

## [1] 1100.621

3d.Add Hearing Status (3-category):

overall_all_AoLE_HearStat <- lm(data=BRIEF_AgeOf, formula = GEC_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+ Hearing_Level_3_Cat)
summary(overall_all_AoLE_HearStat)

## 
## Call:
## lm(formula = GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. + 
##     Hearing_Level_3_Cat, data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -53.778 -14.321  -0.354  10.311  65.300 
## 
## Coefficients:
##                                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                        102.69671   11.90918   8.623 4.86e-14 ***
## SES..3.66.                          -0.23675    0.13238  -1.788  0.07641 .  
## SexMale                             -0.98225    3.64774  -0.269  0.78821    
## AgeMonths                           -0.02332    0.16169  -0.144  0.88559    
## Age.of.Language.Exposure..mo.        0.32526    0.12297   2.645  0.00934 ** 
## Hearing_Level_3_CatDeaf             -2.47951    5.07363  -0.489  0.62601    
## Hearing_Level_3_CatHard of Hearing  -1.86876    4.95996  -0.377  0.70706    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 19.35 on 112 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.1094, Adjusted R-squared:  0.06172 
## F-statistic: 2.294 on 6 and 112 DF,  p-value: 0.03985

AIC(overall_all_AoLE_HearStat)

## [1] 1051.63

Compare Models from Step 3a-c to model from Step 2:

anova(overall_all_AoLE, overall_all_AoLE_AoAE)

## Analysis of Variance Table
## 
## Model 1: GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. + 
##     AoAE
##   Res.Df   RSS Df Sum of Sq      F Pr(>F)
## 1    120 43959                           
## 2    119 43697  1    261.48 0.7121 0.4005

anova(overall_all_AoLE, overall_all_AoLE_Modality)

## Analysis of Variance Table
## 
## Model 1: GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. + 
##     Language_Modality
##   Res.Df   RSS Df Sum of Sq      F Pr(>F)
## 1    120 43959                           
## 2    119 43626  1    332.94 0.9082 0.3425

anova(overall_all_AoLE, overall_all_AoLE_LoAE)

## Analysis of Variance Table
## 
## Model 1: GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. + 
##     Length.of.Auditory.Exposure
##   Res.Df   RSS Df Sum of Sq      F Pr(>F)
## 1    120 43959                           
## 2    119 43804  1    155.19 0.4216 0.5174

Findings: Age of auditory exposure, length of auditory exposure, and language modality are not significant predictors of GEC raw scores, and their addition does not improve model fit.

We cannot do a direct comparison of model in step 3d to the model in step 2 because we are missing hearing status information for 12 participants (mostly Early and Later ASL). However, even in the model in step 3d, Hearing Status is not a significant predictor of BRIEF scores, and Age of Language Exposure is.

Checking assumptions for best-fit GEC model

ols_plot_resid_qq(overall_all_AoLE)

ols_test_normality(overall_all_AoLE)

## -----------------------------------------------
##        Test             Statistic       pvalue  
## -----------------------------------------------
## Shapiro-Wilk              0.9802         0.0638 
## Kolmogorov-Smirnov        0.0657         0.6533 
## Cramer-von Mises          9.9712         0.0000 
## Anderson-Darling          0.7339         0.0544 
## -----------------------------------------------

ols_plot_resid_hist(overall_all_AoLE)

ols_test_correlation(overall_all_AoLE)

## [1] 0.9882637

ols_plot_resid_fit(overall_all_AoLE)

Inhibition Models

Step 1. Base Model:

inhibition_all_base <- lm(data=BRIEF_AgeOf, formula = Inhibit_RawScore~SES..3.66.+Sex+AgeMonths)
summary(inhibition_all_base)

## 
## Call:
## lm(formula = Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths, 
##     data = BRIEF_AgeOf)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -20.3759  -4.6435  -0.0437   4.4091  21.5108 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 22.35171    3.52670   6.338 4.16e-09 ***
## SES..3.66.  -0.08194    0.04086  -2.005   0.0472 *  
## SexMale      0.82837    1.15087   0.720   0.4730    
## AgeMonths    0.08672    0.04436   1.955   0.0529 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.314 on 121 degrees of freedom
## Multiple R-squared:  0.06463,    Adjusted R-squared:  0.04144 
## F-statistic: 2.787 on 3 and 121 DF,  p-value: 0.04365

AIC(inhibition_all_base)

## [1] 821.3795

Step 2. Add Age of Language Exposure:

inhibition_all_AoLE <- lm(data=BRIEF_AgeOf, formula = Inhibit_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.)
summary(inhibition_all_AoLE)

## 
## Call:
## lm(formula = Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths + 
##     Age.of.Language.Exposure..mo., data = BRIEF_AgeOf)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -21.2479  -4.3006  -0.4799   4.2384  22.6940 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   23.28421    3.51600   6.622 1.05e-09 ***
## SES..3.66.                    -0.06801    0.04098  -1.660   0.0996 .  
## SexMale                        0.56967    1.14454   0.498   0.6196    
## AgeMonths                      0.04695    0.04818   0.974   0.3318    
## Age.of.Language.Exposure..mo.  0.06739    0.03392   1.987   0.0492 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.239 on 120 degrees of freedom
## Multiple R-squared:  0.09441,    Adjusted R-squared:  0.06422 
## F-statistic: 3.128 on 4 and 120 DF,  p-value: 0.01737

AIC(inhibition_all_AoLE)

## [1] 819.3348

Compare Model with Age of Language Exposure to base demographic model:

anova(inhibition_all_base, inhibition_all_AoLE)

## Analysis of Variance Table
## 
## Model 1: Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths
## Model 2: Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
##   Res.Df    RSS Df Sum of Sq      F  Pr(>F)  
## 1    121 4824.5                              
## 2    120 4670.9  1    153.61 3.9464 0.04925 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

3a. Add Age of Auditory Experience:

inhibition_all_AoLE_AoAE <- lm(data=BRIEF_AgeOf, formula = Inhibit_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+AoAE)
summary(inhibition_all_AoLE_AoAE)

## 
## Call:
## lm(formula = Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths + 
##     Age.of.Language.Exposure..mo. + AoAE, data = BRIEF_AgeOf)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -21.4387  -4.1619  -0.5115   4.2057  22.4330 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   23.13959    3.53725   6.542  1.6e-09 ***
## SES..3.66.                    -0.07042    0.04135  -1.703   0.0912 .  
## SexMale                        0.59328    1.14887   0.516   0.6065    
## AgeMonths                      0.05440    0.05035   1.080   0.2821    
## Age.of.Language.Exposure..mo.  0.07352    0.03595   2.045   0.0431 *  
## AoAE                          -0.01421    0.02693  -0.528   0.5986    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.258 on 119 degrees of freedom
## Multiple R-squared:  0.09652,    Adjusted R-squared:  0.05856 
## F-statistic: 2.543 on 5 and 119 DF,  p-value: 0.03178

AIC(inhibition_all_AoLE_AoAE)

## [1] 821.0425

3b. Add Length of Auditory Exposure:

inhibition_all_AoLE_LoAE <- lm(data=BRIEF_AgeOf, formula = Inhibit_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+Length.of.Auditory.Exposure)
summary(inhibition_all_AoLE_LoAE)

## 
## Call:
## lm(formula = Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths + 
##     Age.of.Language.Exposure..mo. + Length.of.Auditory.Exposure, 
##     data = BRIEF_AgeOf)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -21.3982  -4.1459  -0.5254   4.3701  22.5208 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   23.18916    3.53761   6.555  1.5e-09 ***
## SES..3.66.                    -0.06947    0.04131  -1.682   0.0952 .  
## SexMale                        0.59306    1.15033   0.516   0.6071    
## AgeMonths                      0.04167    0.05034   0.828   0.4095    
## Age.of.Language.Exposure..mo.  0.07219    0.03634   1.986   0.0493 *  
## Length.of.Auditory.Exposure    0.01017    0.02696   0.377   0.7065    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.261 on 119 degrees of freedom
## Multiple R-squared:  0.09549,    Adjusted R-squared:  0.05749 
## F-statistic: 2.513 on 5 and 119 DF,  p-value: 0.03355

AIC(inhibition_all_AoLE_LoAE)

## [1] 821.1852

3c. Add Modality:

inhibition_all_AoLE_Modality <- lm(data=BRIEF_AgeOf, formula = Inhibit_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+ Language_Modality)
summary(inhibition_all_AoLE_Modality)

## 
## Call:
## lm(formula = Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths + 
##     Age.of.Language.Exposure..mo. + Language_Modality, data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.739  -4.417  -0.699   4.137  22.461 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   23.37747    3.52915   6.624 1.07e-09 ***
## SES..3.66.                    -0.07016    0.04125  -1.701   0.0916 .  
## SexMale                        0.57598    1.14772   0.502   0.6167    
## AgeMonths                      0.05042    0.04867   1.036   0.3024    
## Age.of.Language.Exposure..mo.  0.07357    0.03559   2.067   0.0409 *  
## Language_ModalityASL          -0.75393    1.27919  -0.589   0.5567    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.256 on 119 degrees of freedom
## Multiple R-squared:  0.09704,    Adjusted R-squared:  0.05911 
## F-statistic: 2.558 on 5 and 119 DF,  p-value: 0.03091

AIC(inhibition_all_AoLE_Modality)

## [1] 820.9704

3d. Add Hearing Status (3-category):

inhibition_all_AoLE_HearStat <- lm(data=BRIEF_AgeOf, formula = Inhibit_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+ Hearing_Level_3_Cat)
summary(inhibition_all_AoLE_HearStat)

## 
## Call:
## lm(formula = Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths + 
##     Age.of.Language.Exposure..mo. + Hearing_Level_3_Cat, data = BRIEF_AgeOf)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -20.0506  -4.3309  -0.0461   4.0739  22.5856 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                        24.27720    3.84904   6.307 5.82e-09 ***
## SES..3.66.                         -0.06467    0.04278  -1.511    0.133    
## SexMale                             0.68541    1.17895   0.581    0.562    
## AgeMonths                           0.03202    0.05226   0.613    0.541    
## Age.of.Language.Exposure..mo.       0.05377    0.03974   1.353    0.179    
## Hearing_Level_3_CatDeaf             0.48590    1.63980   0.296    0.768    
## Hearing_Level_3_CatHard of Hearing -1.56960    1.60306  -0.979    0.330    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.254 on 112 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.09152,    Adjusted R-squared:  0.04285 
## F-statistic: 1.881 on 6 and 112 DF,  p-value: 0.09026

AIC(inhibition_all_AoLE_HearStat)

## [1] 782.8125

Compare Models from Step 3a-c to model from Step 2:

anova(inhibition_all_AoLE, inhibition_all_AoLE_AoAE)

## Analysis of Variance Table
## 
## Model 1: Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. + 
##     AoAE
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1    120 4670.9                           
## 2    119 4660.0  1     10.91 0.2786 0.5986

anova(inhibition_all_AoLE, inhibition_all_AoLE_Modality)

## Analysis of Variance Table
## 
## Model 1: Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. + 
##     Language_Modality
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1    120 4670.9                           
## 2    119 4657.3  1    13.595 0.3474 0.5567

anova(inhibition_all_AoLE, inhibition_all_AoLE_LoAE)

## Analysis of Variance Table
## 
## Model 1: Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. + 
##     Length.of.Auditory.Exposure
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1    120 4670.9                           
## 2    119 4665.4  1     5.584 0.1424 0.7065

Checking assumptions for best-fit Inhibition model

ols_plot_resid_qq(inhibition_all_AoLE)

ols_test_normality(inhibition_all_AoLE)

## -----------------------------------------------
##        Test             Statistic       pvalue  
## -----------------------------------------------
## Shapiro-Wilk              0.9772         0.0330 
## Kolmogorov-Smirnov        0.0813         0.3807 
## Cramer-von Mises          9.7914         0.0000 
## Anderson-Darling          0.683          0.0727 
## -----------------------------------------------

ols_plot_resid_hist(inhibition_all_AoLE)

ols_test_correlation(inhibition_all_AoLE)

## [1] 0.9853621

ols_plot_resid_fit(inhibition_all_AoLE)

Shift Models

Step 1. Base Model:

shift_all_base <- lm(data=BRIEF_AgeOf, formula = Shift_RawScore~SES..3.66.+Sex+AgeMonths)
summary(shift_all_base)

## 
## Call:
## lm(formula = Shift_RawScore ~ SES..3.66. + Sex + AgeMonths, data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.4632 -2.8668 -0.6954  2.0394 15.2974 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 16.752518   2.046755   8.185 3.17e-13 ***
## SES..3.66.  -0.060132   0.023714  -2.536   0.0125 *  
## SexMale     -0.234463   0.667921  -0.351   0.7262    
## AgeMonths    0.008393   0.025743   0.326   0.7450    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.665 on 121 degrees of freedom
## Multiple R-squared:  0.05149,    Adjusted R-squared:  0.02797 
## F-statistic: 2.189 on 3 and 121 DF,  p-value: 0.09279

AIC(shift_all_base)

## [1] 685.3529

Step 2. Add Age of Language Exposure:

shift_all_AoLE <- lm(data=BRIEF_AgeOf, formula = Shift_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.)
summary(shift_all_AoLE)

## 
## Call:
## lm(formula = Shift_RawScore ~ SES..3.66. + Sex + AgeMonths + 
##     Age.of.Language.Exposure..mo., data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.9782 -2.4040 -0.3517  1.9774 14.0519 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   17.70857    1.96810   8.998 4.15e-15 ***
## SES..3.66.                    -0.04585    0.02294  -1.999 0.047874 *  
## SexMale                       -0.49970    0.64067  -0.780 0.436942    
## AgeMonths                     -0.03239    0.02697  -1.201 0.232180    
## Age.of.Language.Exposure..mo.  0.06909    0.01899   3.639 0.000405 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.492 on 120 degrees of freedom
## Multiple R-squared:  0.1457, Adjusted R-squared:  0.1173 
## F-statistic: 5.118 on 4 and 120 DF,  p-value: 0.0007661

AIC(shift_all_AoLE)

## [1] 674.271

Compare Model with Age of Language Exposure to base demographic model:

anova(shift_all_base, shift_all_AoLE)

## Analysis of Variance Table
## 
## Model 1: Shift_RawScore ~ SES..3.66. + Sex + AgeMonths
## Model 2: Shift_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
##   Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
## 1    121 1625.0                                  
## 2    120 1463.5  1    161.47 13.239 0.0004055 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

3a. Add Age of Auditory Experience:

shift_all_AoLE_AoAE <- lm(data=BRIEF_AgeOf, formula = Shift_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+AoAE)
summary(shift_all_AoLE_AoAE)

## 
## Call:
## lm(formula = Shift_RawScore ~ SES..3.66. + Sex + AgeMonths + 
##     Age.of.Language.Exposure..mo. + AoAE, data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.8804 -2.3444 -0.3614  2.1039 14.0925 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   17.672586   1.981853   8.917 6.81e-15 ***
## SES..3.66.                    -0.046451   0.023170  -2.005 0.047258 *  
## SexMale                       -0.493827   0.643691  -0.767 0.444494    
## AgeMonths                     -0.030535   0.028208  -1.082 0.281235    
## Age.of.Language.Exposure..mo.  0.070618   0.020144   3.506 0.000643 ***
## AoAE                          -0.003536   0.015088  -0.234 0.815094    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.506 on 119 degrees of freedom
## Multiple R-squared:  0.1461, Adjusted R-squared:  0.1103 
## F-statistic: 4.073 on 5 and 119 DF,  p-value: 0.001896

AIC(shift_all_AoLE_AoAE)

## [1] 676.2133

3b. Add Length of Auditory Exposure:

shift_all_AoLE_LoAE <- lm(data=BRIEF_AgeOf, formula = Shift_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+Length.of.Auditory.Exposure)  
summary(shift_all_AoLE_LoAE)

## 
## Call:
## lm(formula = Shift_RawScore ~ SES..3.66. + Sex + AgeMonths + 
##     Age.of.Language.Exposure..mo. + Length.of.Auditory.Exposure, 
##     data = BRIEF_AgeOf)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -6.903 -2.339 -0.355  2.083 14.085 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   17.681350   1.981072   8.925 6.52e-15 ***
## SES..3.66.                    -0.046269   0.023132  -2.000 0.047752 *  
## SexMale                       -0.493004   0.644186  -0.765 0.445601    
## AgeMonths                     -0.033900   0.028191  -1.203 0.231554    
## Age.of.Language.Exposure..mo.  0.070468   0.020352   3.462 0.000745 ***
## Length.of.Auditory.Exposure    0.002913   0.015096   0.193 0.847317    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.506 on 119 degrees of freedom
## Multiple R-squared:  0.146,  Adjusted R-squared:  0.1101 
## F-statistic: 4.069 on 5 and 119 DF,  p-value: 0.001911

AIC(shift_all_AoLE_LoAE)

## [1] 676.2319

3c. Add Modality:

shift_all_AoLE_Modality <- lm(data=BRIEF_AgeOf, formula = Shift_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+Language_Modality)  
summary(shift_all_AoLE_Modality)

## 
## Call:
## lm(formula = Shift_RawScore ~ SES..3.66. + Sex + AgeMonths + 
##     Age.of.Language.Exposure..mo. + Language_Modality, data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.9964 -2.4716 -0.3886  1.9101 14.0187 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   17.69751    1.97821   8.946 5.82e-15 ***
## SES..3.66.                    -0.04560    0.02312  -1.972  0.05094 .  
## SexMale                       -0.50045    0.64334  -0.778  0.43817    
## AgeMonths                     -0.03280    0.02728  -1.202  0.23167    
## Age.of.Language.Exposure..mo.  0.06836    0.01995   3.426  0.00084 ***
## Language_ModalityASL           0.08940    0.71703   0.125  0.90099    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.507 on 119 degrees of freedom
## Multiple R-squared:  0.1458, Adjusted R-squared:   0.11 
## F-statistic: 4.064 on 5 and 119 DF,  p-value: 0.001929

AIC(shift_all_AoLE_Modality)

## [1] 676.2547

3d. Add Hearing Status (3-category):

shift_all_AoLE_HearStat <- lm(data=BRIEF_AgeOf, formula = Shift_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+ Hearing_Level_3_Cat)
summary(shift_all_AoLE_HearStat)

## 
## Call:
## lm(formula = Shift_RawScore ~ SES..3.66. + Sex + AgeMonths + 
##     Age.of.Language.Exposure..mo. + Hearing_Level_3_Cat, data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.1751 -2.5515 -0.1856  1.7464 13.0271 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                        17.15085    2.14013   8.014 1.16e-12 ***
## SES..3.66.                         -0.04774    0.02379  -2.007  0.04718 *  
## SexMale                            -0.24433    0.65551  -0.373  0.71005    
## AgeMonths                          -0.02479    0.02906  -0.853  0.39538    
## Age.of.Language.Exposure..mo.       0.05993    0.02210   2.712  0.00775 ** 
## Hearing_Level_3_CatDeaf            -0.25406    0.91175  -0.279  0.78103    
## Hearing_Level_3_CatHard of Hearing  1.46543    0.89133   1.644  0.10296    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.478 on 112 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.1649, Adjusted R-squared:  0.1202 
## F-statistic: 3.687 on 6 and 112 DF,  p-value: 0.002216

AIC(shift_all_AoLE_HearStat)

## [1] 643.1168

Compare Models from Step 3a-c to model from Step 2:

anova(shift_all_AoLE, shift_all_AoLE_AoAE)

## Analysis of Variance Table
## 
## Model 1: Shift_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: Shift_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. + 
##     AoAE
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1    120 1463.5                           
## 2    119 1462.8  1   0.67529 0.0549 0.8151

anova(shift_all_AoLE, shift_all_AoLE_Modality)

## Analysis of Variance Table
## 
## Model 1: Shift_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: Shift_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. + 
##     Language_Modality
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1    120 1463.5                           
## 2    119 1463.3  1   0.19114 0.0155  0.901

anova(shift_all_AoLE, shift_all_AoLE_LoAE)

## Analysis of Variance Table
## 
## Model 1: Shift_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: Shift_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. + 
##     Length.of.Auditory.Exposure
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1    120 1463.5                           
## 2    119 1463.1  1   0.45779 0.0372 0.8473

Checking assumptions for best-fit Shift model

ols_plot_resid_qq(shift_all_AoLE)

ols_test_normality(shift_all_AoLE)

## -----------------------------------------------
##        Test             Statistic       pvalue  
## -----------------------------------------------
## Shapiro-Wilk              0.938          0.0000 
## Kolmogorov-Smirnov        0.0833         0.3509 
## Cramer-von Mises         10.2889         0.0000 
## Anderson-Darling          1.6493          3e-04 
## -----------------------------------------------

ols_plot_resid_hist(shift_all_AoLE)

ols_test_correlation(shift_all_AoLE)

## [1] 0.9667448

ols_plot_resid_fit(shift_all_AoLE)

#### Emotional Control Models Step 1. Base Model:

emotctrl_all_base <- lm(data=BRIEF_AgeOf, formula = Emotional.Control_RawScore~SES..3.66.+Sex+AgeMonths)
summary(emotctrl_all_base)

## 
## Call:
## lm(formula = Emotional.Control_RawScore ~ SES..3.66. + Sex + 
##     AgeMonths, data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.1533 -3.0615 -0.6865  2.3701 14.1351 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 17.43032    2.24218   7.774  2.8e-12 ***
## SES..3.66.  -0.06219    0.02598  -2.394   0.0182 *  
## SexMale     -0.39127    0.73170  -0.535   0.5938    
## AgeMonths    0.01649    0.02820   0.585   0.5597    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.015 on 121 degrees of freedom
## Multiple R-squared:  0.049,  Adjusted R-squared:  0.02542 
## F-statistic: 2.078 on 3 and 121 DF,  p-value: 0.1066

Step 2. Add Age of Language Exposure:

emotctrl_all_AoLE <- lm(data=BRIEF_AgeOf, formula = Emotional.Control_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.)
summary(emotctrl_all_AoLE)

## 
## Call:
## lm(formula = Emotional.Control_RawScore ~ SES..3.66. + Sex + 
##     AgeMonths + Age.of.Language.Exposure..mo., data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.0073 -2.8455 -0.6204  2.6420 13.5066 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   17.912837   2.247759   7.969 1.04e-12 ***
## SES..3.66.                    -0.054985   0.026197  -2.099   0.0379 *  
## SexMale                       -0.525139   0.731700  -0.718   0.4743    
## AgeMonths                     -0.004089   0.030803  -0.133   0.8946    
## Age.of.Language.Exposure..mo.  0.034871   0.021687   1.608   0.1105    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.989 on 120 degrees of freedom
## Multiple R-squared:  0.06906,    Adjusted R-squared:  0.03803 
## F-statistic: 2.225 on 4 and 120 DF,  p-value: 0.07024

Compare Model with Age of Language Exposure to base demographic model:

anova(emotctrl_all_base, emotctrl_all_AoLE)

## Analysis of Variance Table
## 
## Model 1: Emotional.Control_RawScore ~ SES..3.66. + Sex + AgeMonths
## Model 2: Emotional.Control_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1    121 1950.1                           
## 2    120 1909.0  1    41.129 2.5854 0.1105

3a. Add Age of Auditory Experience:

emotctrl_all_AoLE_AoAE <- lm(data=BRIEF_AgeOf, formula = Emotional.Control_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+AoAE)
summary(emotctrl_all_AoLE_AoAE)

## 
## Call:
## lm(formula = Emotional.Control_RawScore ~ SES..3.66. + Sex + 
##     AgeMonths + Age.of.Language.Exposure..mo. + AoAE, data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.1383 -2.8960 -0.6379  2.6331 13.6172 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   17.8147979  2.2610112   7.879 1.74e-12 ***
## SES..3.66.                    -0.0566203  0.0264338  -2.142   0.0342 *  
## SexMale                       -0.5091322  0.7343598  -0.693   0.4895    
## AgeMonths                      0.0009605  0.0321817   0.030   0.9762    
## Age.of.Language.Exposure..mo.  0.0390263  0.0229814   1.698   0.0921 .  
## AoAE                          -0.0096359  0.0172137  -0.560   0.5767    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4 on 119 degrees of freedom
## Multiple R-squared:  0.0715, Adjusted R-squared:  0.03249 
## F-statistic: 1.833 on 5 and 119 DF,  p-value: 0.1115

3b. Add Length of Auditory Experience:

emotctrl_all_AoLE_LoAE <- lm(data=BRIEF_AgeOf, formula = Emotional.Control_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+Length.of.Auditory.Exposure)
summary(emotctrl_all_AoLE_LoAE)

## 
## Call:
## lm(formula = Emotional.Control_RawScore ~ SES..3.66. + Sex + 
##     AgeMonths + Age.of.Language.Exposure..mo. + Length.of.Auditory.Exposure, 
##     data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.1219 -2.8985 -0.6491  2.6069 13.6073 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   17.829274   2.260367   7.888 1.66e-12 ***
## SES..3.66.                    -0.056269   0.026393  -2.132   0.0351 *  
## SexMale                       -0.504576   0.735005  -0.686   0.4937    
## AgeMonths                     -0.008732   0.032165  -0.271   0.7865    
## Age.of.Language.Exposure..mo.  0.039090   0.023222   1.683   0.0949 .  
## Length.of.Auditory.Exposure    0.008944   0.017224   0.519   0.6045    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.001 on 119 degrees of freedom
## Multiple R-squared:  0.07116,    Adjusted R-squared:  0.03214 
## F-statistic: 1.823 on 5 and 119 DF,  p-value: 0.1134

3c. Add Modality:

emotctrl_all_AoLE_Modality <- lm(data=BRIEF_AgeOf, formula = Emotional.Control_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+Language_Modality)
summary(emotctrl_all_AoLE_Modality)

## 
## Call:
## lm(formula = Emotional.Control_RawScore ~ SES..3.66. + Sex + 
##     AgeMonths + Age.of.Language.Exposure..mo. + Language_Modality, 
##     data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.0074 -2.8456 -0.6204  2.6421 13.5066 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   17.9128491  2.2594559   7.928 1.35e-12 ***
## SES..3.66.                    -0.0549852  0.0264098  -2.082   0.0395 *  
## SexMale                       -0.5251381  0.7348004  -0.715   0.4762    
## AgeMonths                     -0.0040882  0.0311610  -0.131   0.8958    
## Age.of.Language.Exposure..mo.  0.0348719  0.0227881   1.530   0.1286    
## Language_ModalityASL          -0.0001008  0.8189740   0.000   0.9999    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.005 on 119 degrees of freedom
## Multiple R-squared:  0.06906,    Adjusted R-squared:  0.02994 
## F-statistic: 1.766 on 5 and 119 DF,  p-value: 0.1251

3d. Add Hearing Status (3-category):

emotctrl_all_AoLE_HearStat <- lm(data=BRIEF_AgeOf, formula = Emotional.Control_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+ Hearing_Level_3_Cat)
summary(emotctrl_all_AoLE_HearStat)

## 
## Call:
## lm(formula = Emotional.Control_RawScore ~ SES..3.66. + Sex + 
##     AgeMonths + Age.of.Language.Exposure..mo. + Hearing_Level_3_Cat, 
##     data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.7606 -2.6841 -0.5356  2.8101 13.5574 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                        18.69331    2.46357   7.588 1.04e-11 ***
## SES..3.66.                         -0.05922    0.02738  -2.163   0.0327 *  
## SexMale                            -0.41592    0.75458  -0.551   0.5826    
## AgeMonths                          -0.00406    0.03345  -0.121   0.9036    
## Age.of.Language.Exposure..mo.       0.04673    0.02544   1.837   0.0689 .  
## Hearing_Level_3_CatDeaf            -1.27998    1.04954  -1.220   0.2252    
## Hearing_Level_3_CatHard of Hearing -1.08567    1.02603  -1.058   0.2923    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.003 on 112 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.07064,    Adjusted R-squared:  0.02085 
## F-statistic: 1.419 on 6 and 112 DF,  p-value: 0.2136

No model comparisons or assumption checking for best-fit Emotional Control model because we did not find one that was a good fit for the data

Working Memory Models

Step 1. Base Model:

wm_all_base <- lm(data=BRIEF_AgeOf, formula = Working.Memory_RawScore~SES..3.66.+Sex+AgeMonths)
summary(wm_all_base)

## 
## Call:
## lm(formula = Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths, 
##     data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -11.558  -5.064  -1.554   3.574  23.317 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 23.21624    3.47771   6.676 7.88e-10 ***
## SES..3.66.  -0.04614    0.04029  -1.145    0.254    
## SexMale     -0.37851    1.13489  -0.334    0.739    
## AgeMonths    0.05260    0.04374   1.202    0.232    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.227 on 121 degrees of freedom
## Multiple R-squared:  0.02367,    Adjusted R-squared:  -0.0005401 
## F-statistic: 0.9777 on 3 and 121 DF,  p-value: 0.4057

AIC(wm_all_base)

## [1] 817.8824

Step 2. Add Age of Language Exposure:

wm_all_AoLE <- lm(data=BRIEF_AgeOf, formula = Working.Memory_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.)
summary(wm_all_AoLE)

## 
## Call:
## lm(formula = Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths + 
##     Age.of.Language.Exposure..mo., data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -12.802  -4.034  -1.551   3.346  25.005 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   24.54686    3.40422   7.211 5.41e-11 ***
## SES..3.66.                    -0.02627    0.03968  -0.662  0.50919    
## SexMale                       -0.74767    1.10816  -0.675  0.50117    
## AgeMonths                     -0.00416    0.04665  -0.089  0.92910    
## Age.of.Language.Exposure..mo.  0.09616    0.03284   2.928  0.00409 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.041 on 120 degrees of freedom
## Multiple R-squared:  0.08876,    Adjusted R-squared:  0.05838 
## F-statistic: 2.922 on 4 and 120 DF,  p-value: 0.02394

AIC(wm_all_AoLE)

## [1] 811.258

Compare Model with Age of Language Exposure to base demographic model:

anova(wm_all_base, wm_all_AoLE)

## Analysis of Variance Table
## 
## Model 1: Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths
## Model 2: Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
##   Res.Df    RSS Df Sum of Sq      F   Pr(>F)   
## 1    121 4691.4                                
## 2    120 4378.7  1    312.78 8.5718 0.004086 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

3a. Add Age of Auditory Experience:

wm_all_AoLE_AoAE <- lm(data=BRIEF_AgeOf, formula = Working.Memory_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+AoAE)
summary(wm_all_AoLE_AoAE)

## 
## Call:
## lm(formula = Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths + 
##     Age.of.Language.Exposure..mo. + AoAE, data = BRIEF_AgeOf)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -13.1890  -4.2013  -0.8549   3.4349  24.4761 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   24.25367    3.41120   7.110 9.32e-11 ***
## SES..3.66.                    -0.03116    0.03988  -0.781  0.43618    
## SexMale                       -0.69980    1.10793  -0.632  0.52884    
## AgeMonths                      0.01094    0.04855   0.225  0.82211    
## Age.of.Language.Exposure..mo.  0.10859    0.03467   3.132  0.00219 ** 
## AoAE                          -0.02882    0.02597  -1.110  0.26941    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.035 on 119 degrees of freedom
## Multiple R-squared:  0.09809,    Adjusted R-squared:  0.06019 
## F-statistic: 2.588 on 5 and 119 DF,  p-value: 0.02925

AIC(wm_all_AoLE_AoAE)

## [1] 811.9713

3b. Add Length of Auditory Experience:

wm_all_AoLE_LoAE <- lm(data=BRIEF_AgeOf, formula = Working.Memory_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+Length.of.Auditory.Exposure)
summary(wm_all_AoLE_LoAE)

## 
## Call:
## lm(formula = Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths + 
##     Age.of.Language.Exposure..mo. + Length.of.Auditory.Exposure, 
##     data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -13.126  -4.259  -1.052   3.561  24.632 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   24.34208    3.41704   7.124 8.69e-11 ***
## SES..3.66.                    -0.02941    0.03990  -0.737  0.46243    
## SexMale                       -0.69728    1.11112  -0.628  0.53150    
## AgeMonths                     -0.01554    0.04863  -0.320  0.74987    
## Age.of.Language.Exposure..mo.  0.10650    0.03510   3.034  0.00296 ** 
## Length.of.Auditory.Exposure    0.02192    0.02604   0.842  0.40161    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.048 on 119 degrees of freedom
## Multiple R-squared:  0.09415,    Adjusted R-squared:  0.05609 
## F-statistic: 2.474 on 5 and 119 DF,  p-value: 0.03599

AIC(wm_all_AoLE_LoAE)

## [1] 812.5159

3c. Add Modality:

wm_all_AoLE_Modality <- lm(data=BRIEF_AgeOf, formula = Working.Memory_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+Language_Modality)
summary(wm_all_AoLE_Modality)

## 
## Call:
## lm(formula = Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths + 
##     Age.of.Language.Exposure..mo. + Language_Modality, data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -12.037  -4.310  -1.232   3.512  24.655 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   24.686931   3.409935   7.240 4.81e-11 ***
## SES..3.66.                    -0.029494   0.039857  -0.740  0.46076    
## SexMale                       -0.738184   1.108949  -0.666  0.50692    
## AgeMonths                      0.001049   0.047028   0.022  0.98224    
## Age.of.Language.Exposure..mo.  0.105439   0.034391   3.066  0.00269 ** 
## Language_ModalityASL          -1.132359   1.235983  -0.916  0.36144    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.045 on 119 degrees of freedom
## Multiple R-squared:  0.09514,    Adjusted R-squared:  0.05712 
## F-statistic: 2.502 on 5 and 119 DF,  p-value: 0.03418

AIC(wm_all_AoLE_Modality)

## [1] 812.3794

3d. Add Hearing Status (3-category):

wm_all_AoLE_HearStat <- lm(data=BRIEF_AgeOf, formula = Working.Memory_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+ Hearing_Level_3_Cat)
summary(wm_all_AoLE_HearStat)

## 
## Call:
## lm(formula = Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths + 
##     Age.of.Language.Exposure..mo. + Hearing_Level_3_Cat, data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -12.987  -4.224  -1.291   3.498  25.012 
## 
## Coefficients:
##                                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                        24.458114   3.766083   6.494 2.37e-09 ***
## SES..3.66.                         -0.020635   0.041862  -0.493   0.6230    
## SexMale                            -0.456516   1.153538  -0.396   0.6930    
## AgeMonths                          -0.006845   0.051132  -0.134   0.8937    
## Age.of.Language.Exposure..mo.       0.092197   0.038887   2.371   0.0195 *  
## Hearing_Level_3_CatDeaf            -0.145934   1.604453  -0.091   0.9277    
## Hearing_Level_3_CatHard of Hearing -0.152400   1.568506  -0.097   0.9228    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.12 on 112 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.0741, Adjusted R-squared:  0.0245 
## F-statistic: 1.494 on 6 and 112 DF,  p-value: 0.1866

AIC(wm_all_AoLE_HearStat)

## [1] 777.6269

Compare Models from Step 3a-c to model from Step 2:

anova(wm_all_AoLE, wm_all_AoLE_AoAE)

## Analysis of Variance Table
## 
## Model 1: Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. + 
##     AoAE
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1    120 4378.7                           
## 2    119 4333.8  1     44.84 1.2312 0.2694

anova(wm_all_AoLE, wm_all_AoLE_Modality)

## Analysis of Variance Table
## 
## Model 1: Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. + 
##     Language_Modality
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1    120 4378.7                           
## 2    119 4348.0  1    30.668 0.8394 0.3614

anova(wm_all_AoLE, wm_all_AoLE_LoAE)

## Analysis of Variance Table
## 
## Model 1: Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. + 
##     Length.of.Auditory.Exposure
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1    120 4378.7                           
## 2    119 4352.8  1    25.918 0.7086 0.4016

Checking assumptions for best-fit Working Memory model

ols_plot_resid_qq(wm_all_AoLE)

ols_test_normality(wm_all_AoLE)

## -----------------------------------------------
##        Test             Statistic       pvalue  
## -----------------------------------------------
## Shapiro-Wilk              0.9368         0.0000 
## Kolmogorov-Smirnov        0.107          0.1145 
## Cramer-von Mises          11.092         0.0000 
## Anderson-Darling          1.9662         0.0000 
## -----------------------------------------------

ols_plot_resid_hist(wm_all_AoLE)

ols_test_correlation(wm_all_AoLE)

## [1] 0.9657503

ols_plot_resid_fit(wm_all_AoLE)

#### Planning/Organization Models Step 1. Base Model:

plan_all_base <- lm(data=BRIEF_AgeOf, formula = Plan.Organize_RawScore~SES..3.66.+Sex+AgeMonths)
summary(plan_all_base)

## 
## Call:
## lm(formula = Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths, 
##     data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.9299 -2.8844 -0.5206  2.5453  9.6781 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 17.162934   2.120799   8.093 5.18e-13 ***
## SES..3.66.  -0.039795   0.024572  -1.620    0.108    
## SexMale      0.060933   0.692084   0.088    0.930    
## AgeMonths   -0.004365   0.026674  -0.164    0.870    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.797 on 121 degrees of freedom
## Multiple R-squared:  0.02205,    Adjusted R-squared:  -0.002197 
## F-statistic: 0.9094 on 3 and 121 DF,  p-value: 0.4387

AIC(plan_all_base)

## [1] 694.2373

Step 2. Add Age of Language Exposure:

plan_all_AoLE <- lm(data=BRIEF_AgeOf, formula = Plan.Organize_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.)
summary(plan_all_AoLE)

## 
## Call:
## lm(formula = Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths + 
##     Age.of.Language.Exposure..mo., data = BRIEF_AgeOf)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -6.705 -2.553 -0.642  2.348  9.331 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   18.17011    2.03551   8.927 6.11e-15 ***
## SES..3.66.                    -0.02475    0.02372  -1.043  0.29889    
## SexMale                       -0.21849    0.66261  -0.330  0.74217    
## AgeMonths                     -0.04733    0.02789  -1.697  0.09237 .  
## Age.of.Language.Exposure..mo.  0.07279    0.01964   3.706  0.00032 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.612 on 120 degrees of freedom
## Multiple R-squared:  0.1225, Adjusted R-squared:  0.09324 
## F-statistic: 4.188 on 4 and 120 DF,  p-value: 0.003286

AIC(plan_all_AoLE)

## [1] 682.6903

Compare Model with Age of Language Exposure to base demographic model:

anova(plan_all_base, plan_all_AoLE)

## Analysis of Variance Table
## 
## Model 1: Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths
## Model 2: Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
##   Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
## 1    121 1744.7                                  
## 2    120 1565.5  1     179.2 13.736 0.0003197 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

3a. Add Age of Auditory Experience:

plan_all_AoLE_AoAE <- lm(data=BRIEF_AgeOf, formula = Plan.Organize_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+AoAE)
summary(plan_all_AoLE_AoAE)

## 
## Call:
## lm(formula = Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths + 
##     Age.of.Language.Exposure..mo. + AoAE, data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.1647 -2.4822 -0.6794  2.5480  8.9344 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   17.95046    2.03366   8.827 1.11e-14 ***
## SES..3.66.                    -0.02841    0.02378  -1.195 0.234421    
## SexMale                       -0.18263    0.66052  -0.276 0.782650    
## AgeMonths                     -0.03601    0.02895  -1.244 0.215880    
## Age.of.Language.Exposure..mo.  0.08210    0.02067   3.972 0.000123 ***
## AoAE                          -0.02159    0.01548  -1.394 0.165825    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.598 on 119 degrees of freedom
## Multiple R-squared:  0.1366, Adjusted R-squared:  0.1003 
## F-statistic: 3.765 on 5 and 119 DF,  p-value: 0.003354

AIC(plan_all_AoLE_AoAE)

## [1] 682.6647

3b. Add Length of Auditory Experience:

plan_all_AoLE_LoAE <- lm(data=BRIEF_AgeOf, formula = Plan.Organize_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+Length.of.Auditory.Exposure)
summary(plan_all_AoLE_LoAE)

## 
## Call:
## lm(formula = Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths + 
##     Age.of.Language.Exposure..mo. + Length.of.Auditory.Exposure, 
##     data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.1407 -2.5071 -0.7567  2.4447  9.0120 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   17.99522    2.03684   8.835 1.06e-14 ***
## SES..3.66.                    -0.02744    0.02378  -1.154 0.250936    
## SexMale                       -0.17545    0.66232  -0.265 0.791541    
## AgeMonths                     -0.05704    0.02898  -1.968 0.051388 .  
## Age.of.Language.Exposure..mo.  0.08162    0.02093   3.900 0.000159 ***
## Length.of.Auditory.Exposure    0.01872    0.01552   1.206 0.230208    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.605 on 119 degrees of freedom
## Multiple R-squared:  0.1331, Adjusted R-squared:  0.09667 
## F-statistic: 3.654 on 5 and 119 DF,  p-value: 0.004125

AIC(plan_all_AoLE_LoAE)

## [1] 683.1718

3c. Add Modality:

plan_all_AoLE_Modality <- lm(data=BRIEF_AgeOf, formula = Plan.Organize_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+Language_Modality)
summary(plan_all_AoLE_Modality)

## 
## Call:
## lm(formula = Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths + 
##     Age.of.Language.Exposure..mo. + Language_Modality, data = BRIEF_AgeOf)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -6.228 -2.581 -0.485  2.473  9.040 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   18.29403    2.03035   9.010 4.11e-15 ***
## SES..3.66.                    -0.02761    0.02373  -1.163  0.24706    
## SexMale                       -0.21009    0.66029  -0.318  0.75090    
## AgeMonths                     -0.04272    0.02800  -1.526  0.12978    
## Age.of.Language.Exposure..mo.  0.08099    0.02048   3.955  0.00013 ***
## Language_ModalityASL          -1.00189    0.73593  -1.361  0.17596    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.599 on 119 degrees of freedom
## Multiple R-squared:  0.136,  Adjusted R-squared:  0.09965 
## F-statistic: 3.745 on 5 and 119 DF,  p-value: 0.003485

AIC(plan_all_AoLE_Modality)

## [1] 682.7585

3d. Add Hearing Status (3-category):

plan_all_AoLE_HearStat <- lm(data=BRIEF_AgeOf, formula = Plan.Organize_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+ Hearing_Level_3_Cat)
summary(plan_all_AoLE_HearStat)

## 
## Call:
## lm(formula = Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths + 
##     Age.of.Language.Exposure..mo. + Hearing_Level_3_Cat, data = BRIEF_AgeOf)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -6.179 -2.575 -0.646  2.259  9.207 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                        18.14514    2.19375   8.271 3.06e-13 ***
## SES..3.66.                         -0.02688    0.02438  -1.102 0.272769    
## SexMale                             0.10185    0.67194   0.152 0.879797    
## AgeMonths                          -0.04375    0.02978  -1.469 0.144690    
## Age.of.Language.Exposure..mo.       0.07955    0.02265   3.512 0.000642 ***
## Hearing_Level_3_CatDeaf            -1.04948    0.93460  -1.123 0.263870    
## Hearing_Level_3_CatHard of Hearing  0.67745    0.91366   0.741 0.459961    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.565 on 112 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.1514, Adjusted R-squared:  0.1059 
## F-statistic: 3.329 on 6 and 112 DF,  p-value: 0.004693

AIC(plan_all_AoLE_HearStat)

## [1] 649.0064

Compare Models from Step 3a-c to model from Step 2:

anova(plan_all_AoLE, plan_all_AoLE_AoAE)

## Analysis of Variance Table
## 
## Model 1: Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. + 
##     AoAE
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1    120 1565.5                           
## 2    119 1540.3  1    25.164 1.9441 0.1658

anova(plan_all_AoLE, plan_all_AoLE_Modality)

## Analysis of Variance Table
## 
## Model 1: Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. + 
##     Language_Modality
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1    120 1565.5                           
## 2    119 1541.5  1    24.008 1.8534  0.176

anova(plan_all_AoLE, plan_all_AoLE_LoAE)

## Analysis of Variance Table
## 
## Model 1: Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. + 
##     Length.of.Auditory.Exposure
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1    120 1565.5                           
## 2    119 1546.6  1    18.903 1.4544 0.2302

Checking assumptions for best-fit Plan/Organize model

ols_plot_resid_qq(plan_all_AoLE)

ols_test_normality(plan_all_AoLE)

## -----------------------------------------------
##        Test             Statistic       pvalue  
## -----------------------------------------------
## Shapiro-Wilk              0.9725         0.0117 
## Kolmogorov-Smirnov        0.0891         0.2737 
## Cramer-von Mises         10.5935         0.0000 
## Anderson-Darling          0.9398         0.0168 
## -----------------------------------------------

ols_plot_resid_hist(plan_all_AoLE)

ols_test_correlation(plan_all_AoLE)

## [1] 0.9876708

ols_plot_resid_fit(plan_all_AoLE)

Table 4.

stargazer(overall_all_AoLE_AoAE, inhibition_all_AoLE_AoAE, shift_all_AoLE_AoAE, emotctrl_all_AoLE_AoAE, wm_all_AoLE_AoAE, plan_all_AoLE_AoAE, type= "html", title = "Linear Regression Results for Raw Scores", align=TRUE, dep.var.labels=c("Global Executive Composite", "Inhibition", "Shift", "Emotional Control", "Working Memory", "Planning/Organization"), covariate.labels=c("Socioeconomic Status (SES)", "Sex (Male)", "Age (Months)", "Age of Exposure to Language (Months)", "Age of Auditory Exposure (Months)"), out="Table4.html")

Figure 1.

G <- ggplot(data=BRIEF_AgeApp, mapping = aes(x= Language_Timing, y=GEC_Tscore)) + geom_boxplot() + geom_dotplot(mapping=aes(fill=Language_Modality), dotsize = 1.1, method = "dotdensity", binaxis = "y", stackdir = "center", alpha=0.8) + theme(text = element_text(size=20), axis.title=element_text(size=20,face="bold"), legend.title=element_text(size=24), legend.text=element_text(size=24), axis.title.x=element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black")) + ylab("Global Executive Composite (T-scores)") + scale_x_discrete(labels=c("Early" = "Early Language", "Later" = "Later Language")) + scale_y_continuous(limits= c(30, 100), breaks= c(30, 40, 50, 60, 70, 80, 90, 100)) + scale_fill_grey(name = "Language Modality", start=0.1, end=0.9, ) + geom_hline(aes(yintercept= 50, linetype = "Mean"), colour= 'black') + geom_hline(aes(yintercept= 65, linetype = "Elevated"), colour= 'grey17') + scale_linetype_manual(name = "Standard Score", values=c("dotted", "longdash"), guide = guide_legend(override.aes = list(color = c("grey17", "black"))))

I <- ggplot(data=BRIEF_AgeApp, mapping = aes(x= Language_Timing, y=Inhibit_Tscore)) + geom_boxplot() + geom_dotplot(mapping=aes(fill=Language_Modality), dotsize = 1.1, method = "dotdensity", binaxis = "y", stackdir = "center", alpha=0.8) + theme(text = element_text(size=20), axis.title=element_text(size=20,face="bold"), legend.title=element_text(size=24), legend.text=element_text(size=24), axis.title.x=element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black")) + ylab("Inhibition (T-scores)") + scale_x_discrete(labels=c("Early" = "Early Language", "Later" = "Later Language")) + scale_y_continuous(limits= c(30, 100), breaks= c(30, 40, 50, 60, 70, 80, 90, 100)) + scale_fill_grey(name = "Language Modality", start=0.1, end=0.9) + geom_hline(aes(yintercept= 50, linetype = "Mean"), colour= 'black') + geom_hline(aes(yintercept= 65, linetype = "Elevated"), colour= 'grey17') + scale_linetype_manual(name = "Standard Score", values=c("dotted", "longdash"), guide = guide_legend(override.aes = list(color = c("grey17", "black"))))

S <- ggplot(data=BRIEF_AgeApp, mapping = aes(x= Language_Timing, y=Shift_Tscore)) + geom_boxplot() + geom_dotplot(mapping=aes(fill=Language_Modality), dotsize = 1.1, method = "dotdensity", binaxis = "y", stackdir = "center", alpha=0.8) + theme(text = element_text(size=20), axis.title=element_text(size=20,face="bold"), legend.title=element_text(size=24), legend.text=element_text(size=24), axis.title.x=element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black")) + ylab("Shift (T-scores)") + scale_x_discrete(labels=c("Early" = "Early Language", "Later" = "Later Language")) + scale_y_continuous(limits= c(30, 100), breaks= c(30, 40, 50, 60, 70, 80, 90, 100)) + scale_fill_grey(name = "Language Modality", start=0.1, end=0.9) + geom_hline(aes(yintercept= 50, linetype = "Mean"), colour= 'black') + geom_hline(aes(yintercept= 65, linetype = "Elevated"), colour= 'grey17') + scale_linetype_manual(name = "Standard Score", values=c("dotted", "longdash"), guide = guide_legend(override.aes = list(color = c("grey17", "black"))))

E <- ggplot(data=BRIEF_AgeApp, mapping = aes(x= Language_Timing, y= Emotional.Control_Tscore)) + geom_boxplot() + geom_dotplot(mapping=aes(fill=Language_Modality), dotsize = 1.1, method = "dotdensity", binaxis = "y", stackdir = "center", alpha=0.8) + theme(text = element_text(size=20), axis.title=element_text(size=20,face="bold"), legend.title=element_text(size=24), legend.text=element_text(size=24), axis.title.x=element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black")) + ylab("Emotional Control (T-scores)") + scale_x_discrete(labels=c("Early" = "Early Language", "Later" = "Later Language")) + scale_y_continuous(limits= c(30, 100), breaks= c(30, 40, 50, 60, 70, 80, 90, 100)) + scale_fill_grey(name = "Language Modality", start=0.1, end=0.9)  + geom_hline(aes(yintercept= 50, linetype = "Mean"), colour= 'black') + geom_hline(aes(yintercept= 65, linetype = "Elevated"), colour= 'grey17') + scale_linetype_manual(name = "Standard Score", values=c("dotted", "longdash"), guide = guide_legend(override.aes = list(color = c("grey17", "black"))))

W <- ggplot(data=BRIEF_AgeApp, mapping = aes(x= Language_Timing, y= Working.Memory_Tscore)) + geom_boxplot() + geom_dotplot(mapping=aes(fill=Language_Modality), dotsize = 1.1, method = "dotdensity", binaxis = "y", stackdir = "center", alpha=0.8) + theme(text = element_text(size=20), axis.title=element_text(size=20,face="bold"), legend.title=element_text(size=24), legend.text=element_text(size=24), axis.title.x=element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black"))  + ylab("Working Memory (T-scores)") + scale_x_discrete(labels=c("Early" = "Early Language", "Later" = "Later Language")) + scale_y_continuous(limits= c(30, 100), breaks= c(30, 40, 50, 60, 70, 80, 90, 100)) + scale_fill_grey(name = "Language Modality", start=0.1, end=0.9)  + geom_hline(aes(yintercept= 50, linetype = "Mean"), colour= 'black') + geom_hline(aes(yintercept= 65, linetype = "Elevated"), colour= 'grey17') + scale_linetype_manual(name = "Standard Score", values=c("dotted", "longdash"), guide = guide_legend(override.aes = list(color = c("grey17", "black"))))

P <- ggplot(data=BRIEF_AgeApp, mapping = aes(x= Language_Timing, y= Plan.Organize_Tscore)) + geom_boxplot() + geom_dotplot(mapping=aes(fill=Language_Modality), dotsize = 1.1, method = "dotdensity", binaxis = "y", stackdir = "center", alpha=0.8) + theme(text = element_text(size=20), axis.title=element_text(size=20,face="bold"), legend.title=element_text(size=24), legend.text=element_text(size=24), axis.title.x=element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black")) + ylab("Plan/Organize (T-scores)") + scale_x_discrete(labels=c("Early" = "Early Language", "Later" = "Later Language")) + scale_y_continuous(limits= c(30, 100), breaks= c(30, 40, 50, 60, 70, 80, 90, 100)) + scale_fill_grey(name = "Language Modality", start=0.1, end=0.9) + geom_hline(aes(yintercept= 50, linetype = "Mean"), colour= 'black') + geom_hline(aes(yintercept= 65, linetype = "Elevated"), colour= 'grey17') + scale_linetype_manual(name = "Standard Score", values=c("dotted", "longdash"), guide = guide_legend(override.aes = list(color = c("grey17", "black"))))

ggarrange(G, I, S, E, W, P, ncol=3, nrow=2, common.legend = TRUE, legend="bottom")

Question 3: Do children with later or degraded access to auditory and/or language input exhibit clinically-significant deficits in executive functioning skills relative to typically-hearing peers?

Table 5 data & analyses

Risk Ratios (Relative Risk) and 95% confidence intervals for Global Executive Composite T-scores

BRIEF_AgeApp_GEC_over65 <- BRIEF_AgeApp %>% 
    group_by(LanguageGroup) %>% 
    summarise("Global Executive Composite T-score < 65 (n)" = sum(GEC_Tscore < 65), "Global Executive Composite T-score > 65 (n)" = sum(GEC_Tscore >= 65))

## `summarise()` ungrouping output (override with `.groups` argument)

BRIEF_AgeApp_GEC_over65 %>%
    kable() %>%  
    kable_styling(bootstrap_options = "striped")

LanguageGroup	Global Executive Composite T-score < 65 (n)	Global Executive Composite T-score > 65 (n)
Typically Hearing	41	3
Early ASL	15	0
Later English	26	3
Later ASL	11	3

GEC_rr <- matrix(c(41, 3, 20, 1, 26, 3, 13, 3), 4, 2, byrow=TRUE)
dimnames(GEC_rr) <- list("Group" = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"), "GEC T-score" = c("Not Clinically Significant", "Clinically Significant"))
GEC_rr

##                    GEC T-score
## Group               Not Clinically Significant Clinically Significant
##   Typically Hearing                         41                      3
##   Early ASL                                 20                      1
##   Later English                             26                      3
##   Later ASL                                 13                      3

riskratio.small(GEC_rr, verbose=TRUE)

## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect

## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect

## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect

## $x
##                    GEC T-score
## Group               Not Clinically Significant Clinically Significant
##   Typically Hearing                         41                      3
##   Early ASL                                 20                      1
##   Later English                             26                      3
##   Later ASL                                 13                      3
## 
## $data
##                    GEC T-score
## Group               Not Clinically Significant Clinically Significant Total
##   Typically Hearing                         41                      3    44
##   Early ASL                                 20                      1    21
##   Later English                             26                      3    29
##   Later ASL                                 13                      3    16
##   Total                                    100                     10   110
## 
## $p.exposed
##                    GEC T-score
## Group               Not Clinically Significant Clinically Significant     Total
##   Typically Hearing                       0.41                    0.3 0.4000000
##   Early ASL                               0.20                    0.1 0.1909091
##   Later English                           0.26                    0.3 0.2636364
##   Later ASL                               0.13                    0.3 0.1454545
##   Total                                   1.00                    1.0 1.0000000
## 
## $p.outcome
##                    GEC T-score
## Group               Not Clinically Significant Clinically Significant Total
##   Typically Hearing                  0.9318182             0.06818182     1
##   Early ASL                          0.9523810             0.04761905     1
##   Later English                      0.8965517             0.10344828     1
##   Later ASL                          0.8125000             0.18750000     1
##   Total                              0.9090909             0.09090909     1
## 
## $measure
##                    risk ratio with 95% C.I.
## Group                estimate      lower    upper
##   Typically Hearing 1.0000000         NA       NA
##   Early ASL         0.5357143 0.05920113 4.847708
##   Later English     1.1637931 0.25197780 5.375134
##   Later ASL         2.1093750 0.47325381 9.401853
## 
## $conf.level
## [1] 0.95
## 
## $p.value
##                    two-sided
## Group               midp.exact fisher.exact chi.square
##   Typically Hearing         NA           NA         NA
##   Early ASL          0.8118073    1.0000000  0.7469897
##   Later English      0.6152304    0.6762336  0.5913861
##   Later ASL          0.2249223    0.3275339  0.1730802
## 
## $correction
## [1] FALSE
## 
## attr(,"method")
## [1] "small sample-adjusted UMLE & normal approx (Wald) CI"

Inhibit Risk Ratios and 95% Confidence Intervals

BRIEF_AgeApp_Inhibit_over65 <- BRIEF_AgeApp %>% 
    group_by(LanguageGroup) %>% 
    summarise("Inhibit T-score < 65 (n)" = sum(Inhibit_Tscore < 65), "Inhibit T-score > 65 (n)" = sum(Inhibit_Tscore >= 65))

## `summarise()` ungrouping output (override with `.groups` argument)

BRIEF_AgeApp_Inhibit_over65 %>%
    kable() %>%  
    kable_styling(bootstrap_options = "striped")

LanguageGroup	Inhibit T-score < 65 (n)	Inhibit T-score > 65 (n)
Typically Hearing	42	2
Early ASL	15	0
Later English	27	2
Later ASL	13	1

Inhibit_rr <- matrix(c(42,2, 19, 2, 27, 2, 15, 1), 4, 2, byrow=TRUE)
dimnames(Inhibit_rr) <- list("Group" = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"), "GEC T-score" = c("Not Clinically Significant", "Clinically Significant"))
Inhibit_rr

##                    GEC T-score
## Group               Not Clinically Significant Clinically Significant
##   Typically Hearing                         42                      2
##   Early ASL                                 19                      2
##   Later English                             27                      2
##   Later ASL                                 15                      1

riskratio.small(Inhibit_rr)

## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect

## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect

## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect

## $data
##                    GEC T-score
## Group               Not Clinically Significant Clinically Significant Total
##   Typically Hearing                         42                      2    44
##   Early ASL                                 19                      2    21
##   Later English                             27                      2    29
##   Later ASL                                 15                      1    16
##   Total                                    103                      7   110
## 
## $measure
##                    risk ratio with 95% C.I.
## Group               estimate      lower    upper
##   Typically Hearing 1.000000         NA       NA
##   Early ASL         1.428571 0.21586676 9.454055
##   Later English     1.034483 0.15425133 6.937733
##   Later ASL         0.937500 0.09109955 9.647756
## 
## $p.value
##                    two-sided
## Group               midp.exact fisher.exact chi.square
##   Typically Hearing         NA           NA         NA
##   Early ASL          0.4839744     0.589206  0.4347680
##   Later English      0.6919416     1.000000  0.6657999
##   Later ASL          0.7836353     1.000000  0.7887764
## 
## $correction
## [1] FALSE
## 
## attr(,"method")
## [1] "small sample-adjusted UMLE & normal approx (Wald) CI"

Shift Risk Ratios and 95% Confidence Intervals

BRIEF_AgeApp_Shift_over65 <- BRIEF_AgeApp %>% 
    group_by(LanguageGroup) %>% 
    summarise("Shift T-score < 65 (n)" = sum(Shift_Tscore < 65), "Shift T-score > 65 (n)" = sum(Shift_Tscore >= 65))

## `summarise()` ungrouping output (override with `.groups` argument)

BRIEF_AgeApp_Shift_over65 %>%
    kable() %>%  
    kable_styling(bootstrap_options = "striped")

LanguageGroup	Shift T-score < 65 (n)	Shift T-score > 65 (n)
Typically Hearing	42	2
Early ASL	15	0
Later English	26	3
Later ASL	13	1

Shift_rr <- matrix(c(42,2,20,1, 26, 3, 15, 1), 4, 2, byrow=TRUE)
dimnames(Shift_rr) <- list("Group" = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"), "Shift T-score" = c("Not Clinically Significant", "Clinically Significant"))
Shift_rr

##                    Shift T-score
## Group               Not Clinically Significant Clinically Significant
##   Typically Hearing                         42                      2
##   Early ASL                                 20                      1
##   Later English                             26                      3
##   Later ASL                                 15                      1

riskratio.small(Shift_rr)

## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect

## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect

## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect

## $data
##                    Shift T-score
## Group               Not Clinically Significant Clinically Significant Total
##   Typically Hearing                         42                      2    44
##   Early ASL                                 20                      1    21
##   Later English                             26                      3    29
##   Later ASL                                 15                      1    16
##   Total                                    103                      7   110
## 
## $measure
##                    risk ratio with 95% C.I.
## Group                estimate      lower    upper
##   Typically Hearing 1.0000000         NA       NA
##   Early ASL         0.7142857 0.06856559 7.441110
##   Later English     1.5517241 0.27600959 8.723783
##   Later ASL         0.9375000 0.09109955 9.647756
## 
## $p.value
##                    two-sided
## Group               midp.exact fisher.exact chi.square
##   Typically Hearing         NA           NA         NA
##   Early ASL          0.9387821    1.0000000  0.9689741
##   Later English      0.3850968    0.3799177  0.3371029
##   Later ASL          0.7836353    1.0000000  0.7887764
## 
## $correction
## [1] FALSE
## 
## attr(,"method")
## [1] "small sample-adjusted UMLE & normal approx (Wald) CI"

Emotional Control Risk Ratios and 95% Confidence Intervals

BRIEF_AgeApp_EmoCtrl_over65 <- BRIEF_AgeApp %>% 
    group_by(LanguageGroup) %>% 
    summarise("Emotional Control T-score < 65 (n)" = sum(Emotional.Control_Tscore < 65), "Emotional Control T-score > 65 (n)" = sum(Emotional.Control_Tscore >= 65))

## `summarise()` ungrouping output (override with `.groups` argument)

BRIEF_AgeApp_EmoCtrl_over65 %>%
    kable() %>%  
    kable_styling(bootstrap_options = "striped")

LanguageGroup	Emotional Control T-score < 65 (n)	Emotional Control T-score > 65 (n)
Typically Hearing	41	3
Early ASL	15	0
Later English	27	2
Later ASL	12	2

EmoCtrl_rr <- matrix(c(41,3,21,0, 27, 2, 14, 2), 4, 2, byrow=TRUE)
dimnames(EmoCtrl_rr) <- list("Group" = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"), "Emotional Control T-score" = c("Not Clinically Significant", "Clinically Significant"))
EmoCtrl_rr

##                    Emotional Control T-score
## Group               Not Clinically Significant Clinically Significant
##   Typically Hearing                         41                      3
##   Early ASL                                 21                      0
##   Later English                             27                      2
##   Later ASL                                 14                      2

riskratio.small(EmoCtrl_rr)

## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect

## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect

## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect

## $data
##                    Emotional Control T-score
## Group               Not Clinically Significant Clinically Significant Total
##   Typically Hearing                         41                      3    44
##   Early ASL                                 21                      0    21
##   Later English                             27                      2    29
##   Later ASL                                 14                      2    16
##   Total                                    103                      7   110
## 
## $measure
##                    risk ratio with 95% C.I.
## Group                estimate     lower    upper
##   Typically Hearing 1.0000000        NA       NA
##   Early ASL         0.0000000 0.0000000      NaN
##   Later English     0.7758621 0.1380048 4.361892
##   Later ASL         1.4062500 0.2581260 7.661139
## 
## $p.value
##                    two-sided
## Group               midp.exact fisher.exact chi.square
##   Typically Hearing         NA           NA         NA
##   Early ASL          0.3032051    0.5451923  0.2205022
##   Later English      0.9732160    1.0000000  0.9896504
##   Later ASL          0.5159187    0.6023050  0.4813214
## 
## $correction
## [1] FALSE
## 
## attr(,"method")
## [1] "small sample-adjusted UMLE & normal approx (Wald) CI"

Working Memory Risk Ratios and 95% Confidence Intervals

BRIEF_AgeApp_WorkMem_over65 <- BRIEF_AgeApp %>% 
    group_by(LanguageGroup) %>% 
    summarise("Working Memory T-score < 65 (n)" = sum(Working.Memory_Tscore < 65), "Working Memory T-score > 65 (n)" = sum(Working.Memory_Tscore >= 65))

## `summarise()` ungrouping output (override with `.groups` argument)

BRIEF_AgeApp_WorkMem_over65 %>%
    kable() %>%  
    kable_styling(bootstrap_options = "striped")

LanguageGroup	Working Memory T-score < 65 (n)	Working Memory T-score > 65 (n)
Typically Hearing	39	5
Early ASL	14	1
Later English	25	4
Later ASL	12	2

WorkMem_rr <- matrix(c(39,5,18,3, 25, 4, 14, 2), 4, 2, byrow=TRUE)
dimnames(WorkMem_rr) <- list("Group" = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"), "Working Memory T-score" = c("Not Clinically Significant", "Clinically Significant"))
WorkMem_rr

##                    Working Memory T-score
## Group               Not Clinically Significant Clinically Significant
##   Typically Hearing                         39                      5
##   Early ASL                                 18                      3
##   Later English                             25                      4
##   Later ASL                                 14                      2

riskratio.small(WorkMem_rr)

## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect

## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect

## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect

## $data
##                    Working Memory T-score
## Group               Not Clinically Significant Clinically Significant Total
##   Typically Hearing                         39                      5    44
##   Early ASL                                 18                      3    21
##   Later English                             25                      4    29
##   Later ASL                                 14                      2    16
##   Total                                     96                     14   110
## 
## $measure
##                    risk ratio with 95% C.I.
## Group               estimate     lower    upper
##   Typically Hearing 1.000000        NA       NA
##   Early ASL         1.071429 0.2823442 4.065814
##   Later English     1.034483 0.3028657 3.533430
##   Later ASL         0.937500 0.2016350 4.358897
## 
## $p.value
##                    two-sided
## Group               midp.exact fisher.exact chi.square
##   Typically Hearing         NA           NA         NA
##   Early ASL          0.7373410     0.706303  0.7373589
##   Later English      0.7619234     1.000000  0.7573605
##   Later ASL          0.8792232     1.000000  0.9034907
## 
## $correction
## [1] FALSE
## 
## attr(,"method")
## [1] "small sample-adjusted UMLE & normal approx (Wald) CI"

Plan/Organize Risk Ratios and 95% Confidence Intervals

BRIEF_AgeApp_PlanOrg_over65 <- BRIEF_AgeApp %>% 
    group_by(LanguageGroup) %>% 
    summarise("Plan/Organize T-score < 65 (n)" = sum(Plan.Organize_Tscore < 65), "Plan/Organize T-score > 65 (n)" = sum(Plan.Organize_Tscore >= 65))

## `summarise()` ungrouping output (override with `.groups` argument)

BRIEF_AgeApp_PlanOrg_over65 %>%
    kable() %>%  
    kable_styling(bootstrap_options = "striped")

LanguageGroup	Plan/Organize T-score < 65 (n)	Plan/Organize T-score > 65 (n)
Typically Hearing	41	3
Early ASL	15	0
Later English	25	4
Later ASL	13	1

PlanOrg_rr <- matrix(c(41,3,20,1, 25, 4, 15, 1), 4, 2, byrow=TRUE)
dimnames(PlanOrg_rr) <- list("Group" = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"), "Plan/Organize T-score" = c("Not Clinically Significant", "Clinically Significant"))
PlanOrg_rr

##                    Plan/Organize T-score
## Group               Not Clinically Significant Clinically Significant
##   Typically Hearing                         41                      3
##   Early ASL                                 20                      1
##   Later English                             25                      4
##   Later ASL                                 15                      1

riskratio.small(PlanOrg_rr)

## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect

## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect

## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect

## $data
##                    Plan/Organize T-score
## Group               Not Clinically Significant Clinically Significant Total
##   Typically Hearing                         41                      3    44
##   Early ASL                                 20                      1    21
##   Later English                             25                      4    29
##   Later ASL                                 15                      1    16
##   Total                                    101                      9   110
## 
## $measure
##                    risk ratio with 95% C.I.
## Group                estimate      lower    upper
##   Typically Hearing 1.0000000         NA       NA
##   Early ASL         0.5357143 0.05920113 4.847708
##   Later English     1.5517241 0.37445613 6.430253
##   Later ASL         0.7031250 0.07871935 6.280346
## 
## $p.value
##                    two-sided
## Group               midp.exact fisher.exact chi.square
##   Typically Hearing         NA           NA         NA
##   Early ASL          0.8118073    1.0000000  0.7469897
##   Later English      0.3585273    0.4249522  0.3219848
##   Later ASL          0.9913275    1.0000000  0.9378094
## 
## $correction
## [1] FALSE
## 
## attr(,"method")
## [1] "small sample-adjusted UMLE & normal approx (Wald) CI"

Additional checks

Check 1: Comparing stepwise models with AoAE added first

Possible Issue: Because Age of Language Exposure and Age of Auditory Exposure were significantly correlated, we need to determine whether they might actually be measuring same underlying construct

Approach: See whether findings above (where Language but not Auditory exposure significantly predicts BRIEF scores) hold when predictors are added to the model in a different order

overall_all_base <- lm(data=BRIEF_AgeOf, formula = GEC_RawScore~SES..3.66.+Sex+AgeMonths)
summary(overall_all_base)

## 
## Call:
## lm(formula = GEC_RawScore ~ SES..3.66. + Sex + AgeMonths, data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -49.301 -16.784  -0.489  10.601  60.607 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  96.5244    11.0764   8.714 1.83e-14 ***
## SES..3.66.   -0.3029     0.1283  -2.360   0.0199 *  
## SexMale      -0.6580     3.6146  -0.182   0.8559    
## AgeMonths     0.1772     0.1393   1.272   0.2057    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 19.83 on 121 degrees of freedom
## Multiple R-squared:  0.05657,    Adjusted R-squared:  0.03318 
## F-statistic: 2.419 on 3 and 121 DF,  p-value: 0.06954

overall_all_AoAE <- lm(data=BRIEF_AgeOf, formula = GEC_RawScore~SES..3.66.+Sex+AgeMonths+AoAE)
summary(overall_all_AoAE)

## 
## Call:
## lm(formula = GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE, 
##     data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -49.168 -16.628  -0.379  10.425  61.047 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 96.79798   11.19724   8.645 2.81e-14 ***
## SES..3.66.  -0.29849    0.13054  -2.286    0.024 *  
## SexMale     -0.71423    3.63891  -0.196    0.845    
## AgeMonths    0.16404    0.15345   1.069    0.287    
## AoAE         0.01695    0.08109   0.209    0.835    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 19.91 on 120 degrees of freedom
## Multiple R-squared:  0.05692,    Adjusted R-squared:  0.02548 
## F-statistic:  1.81 on 4 and 120 DF,  p-value: 0.1312

anova(overall_all_base, overall_all_AoAE)

## Analysis of Variance Table
## 
## Model 1: GEC_RawScore ~ SES..3.66. + Sex + AgeMonths
## Model 2: GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE
##   Res.Df   RSS Df Sum of Sq      F Pr(>F)
## 1    121 47591                           
## 2    120 47573  1     17.32 0.0437 0.8348

overall_all_AoAE_AoLE <- lm(data=BRIEF_AgeOf, formula = GEC_RawScore~SES..3.66.+Sex+AgeMonths+AoAE+Age.of.Language.Exposure..mo.)
summary(overall_all_AoAE_AoLE)

## 
## Call:
## lm(formula = GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE + 
##     Age.of.Language.Exposure..mo., data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -54.475 -13.271   0.398  10.333  65.083 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   100.35055   10.83174   9.264 1.03e-15 ***
## SES..3.66.                     -0.24696    0.12664  -1.950  0.05351 .  
## SexMale                        -1.80034    3.51807  -0.512  0.60978    
## AgeMonths                       0.02029    0.15417   0.132  0.89550    
## AoAE                           -0.06959    0.08247  -0.844  0.40045    
## Age.of.Language.Exposure..mo.   0.35769    0.11010   3.249  0.00151 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 19.16 on 119 degrees of freedom
## Multiple R-squared:  0.1338, Adjusted R-squared:  0.09735 
## F-statistic: 3.675 on 5 and 119 DF,  p-value: 0.003968

anova(overall_all_AoAE, overall_all_AoAE_AoLE)

## Analysis of Variance Table
## 
## Model 1: GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE
## Model 2: GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE + Age.of.Language.Exposure..mo.
##   Res.Df   RSS Df Sum of Sq      F   Pr(>F)   
## 1    120 47573                                
## 2    119 43697  1    3875.9 10.555 0.001506 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#~~~~~#
inhibition_all_base <- lm(data=BRIEF_AgeOf, formula = Inhibit_RawScore~SES..3.66.+Sex+AgeMonths)
summary(inhibition_all_base)

## 
## Call:
## lm(formula = Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths, 
##     data = BRIEF_AgeOf)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -20.3759  -4.6435  -0.0437   4.4091  21.5108 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 22.35171    3.52670   6.338 4.16e-09 ***
## SES..3.66.  -0.08194    0.04086  -2.005   0.0472 *  
## SexMale      0.82837    1.15087   0.720   0.4730    
## AgeMonths    0.08672    0.04436   1.955   0.0529 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.314 on 121 degrees of freedom
## Multiple R-squared:  0.06463,    Adjusted R-squared:  0.04144 
## F-statistic: 2.787 on 3 and 121 DF,  p-value: 0.04365

inhibition_all_AoAE <- lm(data=BRIEF_AgeOf, formula = Inhibit_RawScore~SES..3.66.+Sex+AgeMonths+AoAE)
summary(inhibition_all_AoAE)

## 
## Call:
## lm(formula = Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths + 
##     AoAE, data = BRIEF_AgeOf)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -20.3479  -4.6420  -0.1239   4.4228  21.6034 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 22.409374   3.565524   6.285 5.49e-09 ***
## SES..3.66.  -0.081015   0.041569  -1.949   0.0536 .  
## SexMale      0.816525   1.158735   0.705   0.4824    
## AgeMonths    0.083942   0.048864   1.718   0.0884 .  
## AoAE         0.003573   0.025822   0.138   0.8902    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.34 on 120 degrees of freedom
## Multiple R-squared:  0.06478,    Adjusted R-squared:  0.0336 
## F-statistic: 2.078 on 4 and 120 DF,  p-value: 0.08789

anova(inhibition_all_base, inhibition_all_AoAE)

## Analysis of Variance Table
## 
## Model 1: Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths
## Model 2: Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1    121 4824.5                           
## 2    120 4823.8  1   0.76955 0.0191 0.8902

inhibition_all_AoAE_AoLE <- lm(data=BRIEF_AgeOf, formula = Inhibit_RawScore~SES..3.66.+Sex+AgeMonths+AoAE+Age.of.Language.Exposure..mo.)
summary(inhibition_all_AoAE_AoLE)

## 
## Call:
## lm(formula = Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths + 
##     AoAE + Age.of.Language.Exposure..mo., data = BRIEF_AgeOf)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -21.4387  -4.1619  -0.5115   4.2057  22.4330 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   23.13959    3.53725   6.542  1.6e-09 ***
## SES..3.66.                    -0.07042    0.04135  -1.703   0.0912 .  
## SexMale                        0.59328    1.14887   0.516   0.6065    
## AgeMonths                      0.05440    0.05035   1.080   0.2821    
## AoAE                          -0.01421    0.02693  -0.528   0.5986    
## Age.of.Language.Exposure..mo.  0.07352    0.03595   2.045   0.0431 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.258 on 119 degrees of freedom
## Multiple R-squared:  0.09652,    Adjusted R-squared:  0.05856 
## F-statistic: 2.543 on 5 and 119 DF,  p-value: 0.03178

anova(inhibition_all_AoAE, inhibition_all_AoAE_AoLE)

## Analysis of Variance Table
## 
## Model 1: Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE
## Model 2: Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE + Age.of.Language.Exposure..mo.
##   Res.Df    RSS Df Sum of Sq      F  Pr(>F)  
## 1    120 4823.8                              
## 2    119 4660.0  1    163.75 4.1817 0.04307 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#~~~~~#
shift_all_base <- lm(data=BRIEF_AgeOf, formula = Shift_RawScore~SES..3.66.+Sex+AgeMonths) 
summary(shift_all_base)

## 
## Call:
## lm(formula = Shift_RawScore ~ SES..3.66. + Sex + AgeMonths, data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.4632 -2.8668 -0.6954  2.0394 15.2974 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 16.752518   2.046755   8.185 3.17e-13 ***
## SES..3.66.  -0.060132   0.023714  -2.536   0.0125 *  
## SexMale     -0.234463   0.667921  -0.351   0.7262    
## AgeMonths    0.008393   0.025743   0.326   0.7450    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.665 on 121 degrees of freedom
## Multiple R-squared:  0.05149,    Adjusted R-squared:  0.02797 
## F-statistic: 2.189 on 3 and 121 DF,  p-value: 0.09279

shift_all_AoAE <- lm(data=BRIEF_AgeOf, formula = Shift_RawScore~SES..3.66.+Sex+AgeMonths+AoAE)  
summary(shift_all_AoAE)

## 
## Call:
## lm(formula = Shift_RawScore ~ SES..3.66. + Sex + AgeMonths + 
##     AoAE, data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.9645 -2.8688 -0.6796  2.0712 15.0365 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 16.971207   2.062395   8.229 2.62e-13 ***
## SES..3.66.  -0.056624   0.024045  -2.355   0.0201 *  
## SexMale     -0.279398   0.670244  -0.417   0.6775    
## AgeMonths   -0.002155   0.028264  -0.076   0.9393    
## AoAE         0.013549   0.014936   0.907   0.3662    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.667 on 120 degrees of freedom
## Multiple R-squared:  0.05795,    Adjusted R-squared:  0.02654 
## F-statistic: 1.845 on 4 and 120 DF,  p-value: 0.1246

anova(shift_all_base, shift_all_AoAE)

## Analysis of Variance Table
## 
## Model 1: Shift_RawScore ~ SES..3.66. + Sex + AgeMonths
## Model 2: Shift_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1    121 1625.0                           
## 2    120 1613.9  1    11.066 0.8228 0.3662

shift_all_AoAE_AoLE <- lm(data=BRIEF_AgeOf, formula = Shift_RawScore~SES..3.66.+Sex+AgeMonths+AoAE+Age.of.Language.Exposure..mo.)  
summary(shift_all_AoAE_AoLE)

## 
## Call:
## lm(formula = Shift_RawScore ~ SES..3.66. + Sex + AgeMonths + 
##     AoAE + Age.of.Language.Exposure..mo., data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.8804 -2.3444 -0.3614  2.1039 14.0925 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   17.672586   1.981853   8.917 6.81e-15 ***
## SES..3.66.                    -0.046451   0.023170  -2.005 0.047258 *  
## SexMale                       -0.493827   0.643691  -0.767 0.444494    
## AgeMonths                     -0.030535   0.028208  -1.082 0.281235    
## AoAE                          -0.003536   0.015088  -0.234 0.815094    
## Age.of.Language.Exposure..mo.  0.070618   0.020144   3.506 0.000643 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.506 on 119 degrees of freedom
## Multiple R-squared:  0.1461, Adjusted R-squared:  0.1103 
## F-statistic: 4.073 on 5 and 119 DF,  p-value: 0.001896

anova(shift_all_AoAE, shift_all_AoAE_AoLE)

## Analysis of Variance Table
## 
## Model 1: Shift_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE
## Model 2: Shift_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE + Age.of.Language.Exposure..mo.
##   Res.Df    RSS Df Sum of Sq     F   Pr(>F)    
## 1    120 1613.9                                
## 2    119 1462.8  1    151.08 12.29 0.000643 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#~~~~~#
emotctrl_all_base <- lm(data=BRIEF_AgeOf, formula = Emotional.Control_RawScore~SES..3.66.+Sex+AgeMonths)
emotctrl_all_AoAE <- lm(data=BRIEF_AgeOf, formula = Emotional.Control_RawScore~SES..3.66.+Sex+AgeMonths+AoAE)
summary(emotctrl_all_AoAE)

## 
## Call:
## lm(formula = Emotional.Control_RawScore ~ SES..3.66. + Sex + 
##     AgeMonths + AoAE, data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.1562 -3.0647 -0.6891  2.3727 14.1389 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 17.4271894  2.2670480   7.687 4.59e-12 ***
## SES..3.66.  -0.0622427  0.0264307  -2.355   0.0201 *  
## SexMale     -0.3906305  0.7367523  -0.530   0.5969    
## AgeMonths    0.0166440  0.0310691   0.536   0.5931    
## AoAE        -0.0001941  0.0164184  -0.012   0.9906    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.031 on 120 degrees of freedom
## Multiple R-squared:  0.049,  Adjusted R-squared:  0.0173 
## F-statistic: 1.546 on 4 and 120 DF,  p-value: 0.1933

anova(emotctrl_all_base, emotctrl_all_AoAE)

## Analysis of Variance Table
## 
## Model 1: Emotional.Control_RawScore ~ SES..3.66. + Sex + AgeMonths
## Model 2: Emotional.Control_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE
##   Res.Df    RSS Df Sum of Sq     F Pr(>F)
## 1    121 1950.1                          
## 2    120 1950.1  1 0.0022716 1e-04 0.9906

emotctrl_all_AoAE_AoLE <- lm(data=BRIEF_AgeOf, formula = Emotional.Control_RawScore~SES..3.66.+Sex+AgeMonths+AoAE+Age.of.Language.Exposure..mo.)
summary(emotctrl_all_AoAE_AoLE)

## 
## Call:
## lm(formula = Emotional.Control_RawScore ~ SES..3.66. + Sex + 
##     AgeMonths + AoAE + Age.of.Language.Exposure..mo., data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.1383 -2.8960 -0.6379  2.6331 13.6172 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   17.8147979  2.2610112   7.879 1.74e-12 ***
## SES..3.66.                    -0.0566203  0.0264338  -2.142   0.0342 *  
## SexMale                       -0.5091322  0.7343598  -0.693   0.4895    
## AgeMonths                      0.0009605  0.0321817   0.030   0.9762    
## AoAE                          -0.0096359  0.0172137  -0.560   0.5767    
## Age.of.Language.Exposure..mo.  0.0390263  0.0229814   1.698   0.0921 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4 on 119 degrees of freedom
## Multiple R-squared:  0.0715, Adjusted R-squared:  0.03249 
## F-statistic: 1.833 on 5 and 119 DF,  p-value: 0.1115

anova(emotctrl_all_AoAE, emotctrl_all_AoAE_AoLE)

## Analysis of Variance Table
## 
## Model 1: Emotional.Control_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE
## Model 2: Emotional.Control_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE + 
##     Age.of.Language.Exposure..mo.
##   Res.Df    RSS Df Sum of Sq      F  Pr(>F)  
## 1    120 1950.1                              
## 2    119 1904.0  1     46.14 2.8838 0.09209 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#~~~~~#
wm_all_base <- lm(data=BRIEF_AgeOf, formula = Working.Memory_RawScore~SES..3.66.+Sex+AgeMonths)
summary(wm_all_base)

## 
## Call:
## lm(formula = Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths, 
##     data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -11.558  -5.064  -1.554   3.574  23.317 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 23.21624    3.47771   6.676 7.88e-10 ***
## SES..3.66.  -0.04614    0.04029  -1.145    0.254    
## SexMale     -0.37851    1.13489  -0.334    0.739    
## AgeMonths    0.05260    0.04374   1.202    0.232    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.227 on 121 degrees of freedom
## Multiple R-squared:  0.02367,    Adjusted R-squared:  -0.0005401 
## F-statistic: 0.9777 on 3 and 121 DF,  p-value: 0.4057

wm_all_AoAE <- lm(data=BRIEF_AgeOf, formula = Working.Memory_RawScore~SES..3.66.+Sex+AgeMonths+AoAE)
summary(wm_all_AoAE)

## 
## Call:
## lm(formula = Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths + 
##     AoAE, data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -11.578  -5.055  -1.611   3.673  23.251 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 23.175157   3.516130   6.591 1.23e-09 ***
## SES..3.66.  -0.046803   0.040993  -1.142    0.256    
## SexMale     -0.370072   1.142683  -0.324    0.747    
## AgeMonths    0.054579   0.048187   1.133    0.260    
## AoAE        -0.002545   0.025465  -0.100    0.921    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.252 on 120 degrees of freedom
## Multiple R-squared:  0.02375,    Adjusted R-squared:  -0.008794 
## F-statistic: 0.7298 on 4 and 120 DF,  p-value: 0.5733

anova(wm_all_base, wm_all_AoAE)

## Analysis of Variance Table
## 
## Model 1: Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths
## Model 2: Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE
##   Res.Df    RSS Df Sum of Sq    F Pr(>F)
## 1    121 4691.4                         
## 2    120 4691.1  1    0.3906 0.01 0.9205

wm_all_AoAE_AoLE <- lm(data=BRIEF_AgeOf, formula = Working.Memory_RawScore~SES..3.66.+Sex+AgeMonths+AoAE+Age.of.Language.Exposure..mo.)
summary(wm_all_AoAE_AoLE)

## 
## Call:
## lm(formula = Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths + 
##     AoAE + Age.of.Language.Exposure..mo., data = BRIEF_AgeOf)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -13.1890  -4.2013  -0.8549   3.4349  24.4761 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   24.25367    3.41120   7.110 9.32e-11 ***
## SES..3.66.                    -0.03116    0.03988  -0.781  0.43618    
## SexMale                       -0.69980    1.10793  -0.632  0.52884    
## AgeMonths                      0.01094    0.04855   0.225  0.82211    
## AoAE                          -0.02882    0.02597  -1.110  0.26941    
## Age.of.Language.Exposure..mo.  0.10859    0.03467   3.132  0.00219 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.035 on 119 degrees of freedom
## Multiple R-squared:  0.09809,    Adjusted R-squared:  0.06019 
## F-statistic: 2.588 on 5 and 119 DF,  p-value: 0.02925

anova(wm_all_AoAE, wm_all_AoAE_AoLE)

## Analysis of Variance Table
## 
## Model 1: Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE
## Model 2: Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE + 
##     Age.of.Language.Exposure..mo.
##   Res.Df    RSS Df Sum of Sq      F   Pr(>F)   
## 1    120 4691.1                                
## 2    119 4333.8  1    357.23 9.8088 0.002186 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#~~~~~#
plan_all_base <- lm(data=BRIEF_AgeOf, formula = Plan.Organize_RawScore~SES..3.66.+Sex+AgeMonths)
summary(plan_all_base)

## 
## Call:
## lm(formula = Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths, 
##     data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.9299 -2.8844 -0.5206  2.5453  9.6781 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 17.162934   2.120799   8.093 5.18e-13 ***
## SES..3.66.  -0.039795   0.024572  -1.620    0.108    
## SexMale      0.060933   0.692084   0.088    0.930    
## AgeMonths   -0.004365   0.026674  -0.164    0.870    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.797 on 121 degrees of freedom
## Multiple R-squared:  0.02205,    Adjusted R-squared:  -0.002197 
## F-statistic: 0.9094 on 3 and 121 DF,  p-value: 0.4387

plan_all_AoAE <- lm(data=BRIEF_AgeOf, formula = Plan.Organize_RawScore~SES..3.66.+Sex+AgeMonths+AoAE)
summary(plan_all_AoAE)

## 
## Call:
## lm(formula = Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths + 
##     AoAE, data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.9602 -2.9064 -0.4391  2.5251  9.6625 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 17.135079   2.144209   7.991 9.25e-13 ***
## SES..3.66.  -0.040242   0.024999  -1.610    0.110    
## SexMale      0.066657   0.696832   0.096    0.924    
## AgeMonths   -0.003021   0.029386  -0.103    0.918    
## AoAE        -0.001726   0.015529  -0.111    0.912    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.813 on 120 degrees of freedom
## Multiple R-squared:  0.02215,    Adjusted R-squared:  -0.01044 
## F-statistic: 0.6796 on 4 and 120 DF,  p-value: 0.6074

anova(plan_all_base, plan_all_AoAE)

## Analysis of Variance Table
## 
## Model 1: Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths
## Model 2: Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1    121 1744.7                           
## 2    120 1744.5  1   0.17953 0.0123 0.9117

plan_all_AoAE_AoLE <- lm(data=BRIEF_AgeOf, formula = Plan.Organize_RawScore~SES..3.66.+Sex+AgeMonths+AoAE+Age.of.Language.Exposure..mo.)
summary(plan_all_AoAE_AoLE)

## 
## Call:
## lm(formula = Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths + 
##     AoAE + Age.of.Language.Exposure..mo., data = BRIEF_AgeOf)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.1647 -2.4822 -0.6794  2.5480  8.9344 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   17.95046    2.03366   8.827 1.11e-14 ***
## SES..3.66.                    -0.02841    0.02378  -1.195 0.234421    
## SexMale                       -0.18263    0.66052  -0.276 0.782650    
## AgeMonths                     -0.03601    0.02895  -1.244 0.215880    
## AoAE                          -0.02159    0.01548  -1.394 0.165825    
## Age.of.Language.Exposure..mo.  0.08210    0.02067   3.972 0.000123 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.598 on 119 degrees of freedom
## Multiple R-squared:  0.1366, Adjusted R-squared:  0.1003 
## F-statistic: 3.765 on 5 and 119 DF,  p-value: 0.003354

anova(plan_all_AoAE, plan_all_AoAE_AoLE)

## Analysis of Variance Table
## 
## Model 1: Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE
## Model 2: Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE + 
##     Age.of.Language.Exposure..mo.
##   Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
## 1    120 1744.5                                  
## 2    119 1540.3  1    204.18 15.774 0.0001227 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Findings: For all subscales, adding Age of Auditory Exposure to the model with demographic variables did not improve model fit (and Age of Auditory Exposure was not a significant predictor of any subsale), but subsequently adding Age of Language exposure to the model with Age of Auditory Exposure and demographic variables did significantly improve model fit (and Age of Language Exposure was a significant predictor of BRIEF-P scores in almost all models).

Check 2: Age-appropriate vs. non-age-appropriate participants

29 participants in the models included in this paper were outside the normed age range for the BRIEF-P (this is why we used raw scores in models above)

Possible issue: If this “non-age-appropriate” group of participants is largely from the Early ASL group, and/or has higher SES, our finding that Early exposure to language is the driving factor in EF development may not be valid

Approach: Checking the Age, SES, and Language Group of participants who were “age-appropriate” for the BRIEF-P compared to those who were not

table1::label(BRIEF_AgeOf$AgeMonths) <- "Age (Months)"
table1::label(BRIEF_AgeOf$SES..3.66.) <- "SES"
table1::label(BRIEF_AgeOf$LanguageGroup) <- "Language Group"


table1::table1(~AgeMonths + SES..3.66. + LanguageGroup | Age.Appropriate., data = BRIEF_AgeOf, overall=T)

	no (N=23)	yes (N=102)	TRUE (N=125)
Age (Months)
Mean (SD)	77.8 (5.70)	54.4 (9.80)	58.7 (12.9)
Median [Min, Max]	76.0 [72.0, 91.0]	55.0 [37.0, 71.0]	58.0 [37.0, 91.0]
SES
Mean (SD)	47.0 (20.0)	51.7 (12.2)	50.8 (14.0)
Median [Min, Max]	58.5 [8.00, 66.0]	54.3 [3.00, 66.0]	54.5 [3.00, 66.0]
Language Group
Typically Hearing	2 (8.7%)	44 (43.1%)	46 (36.8%)
Early ASL	5 (21.7%)	15 (14.7%)	20 (16.0%)
Later English	6 (26.1%)	29 (28.4%)	35 (28.0%)
Later ASL	10 (43.5%)	14 (13.7%)	24 (19.2%)

Findings:

• Overall, Later-exposed groups make up 70% of the non-age-appropriate group.
• ASL Later kids make up 48% of the non age-appropriate sample, 14.5% of the age-appropriate sample, and 21.6% of the overall sample
• English later kids make up 20.7% of the non age-appropriate sample, 26.4% of the age-appropriate sample, and 25.2% of the overall sample

Participants in the Later groups, specifically the Later ASL group, are over-represented in not having received age-appropriate BRIEF. This should not be a concern for our findings, because if the behaviors surveyed in the BRIEF-P were not appropriate for older kids, then we would not expect the older Later ASL group to have elevated BRIEF scores relative to early-exposed kids–but they do.

Approach Pt 2: Re-run Chi-square with only age-appropriate subset to confirm the non-difference isn’t driven by older Early ASL participants Create dataframe with only age-appropriate participants exposed to language early

BRIEF_early_AgeApp <- subset(BRIEF_AgeApp, BRIEF_AgeApp$Language_Timing=="Early")

BRIEF Scores & Welch two sample t-tests for two “Early” groups within Age-appropriate subset

table1::label(BRIEF_early_AgeApp$GEC_RawScore) <- "Global Executive Composite"
table1::label(BRIEF_early_AgeApp$Inhibit_RawScore) <- "Inhibition"
table1::label(BRIEF_early_AgeApp$Shift_RawScore) <- "Shift"
table1::label(BRIEF_early_AgeApp$Emotional.Control_RawScore) <- "Emotional Control"
table1::label(BRIEF_early_AgeApp$Working.Memory_RawScore) <- "Working Memory"
table1::label(BRIEF_early_AgeApp$Plan.Organize_RawScore) <- "Plan/Organize"

table1(~GEC_RawScore + Inhibit_RawScore + Shift_RawScore + Emotional.Control_RawScore + Working.Memory_RawScore + Plan.Organize_RawScore | Language_Modality, data = BRIEF_early_AgeApp, overall=F)

	English (N=44)	ASL (N=15)
Global Executive Composite
Mean (SD)	88.4 (18.9)	85.8 (13.5)
Median [Min, Max]	88.0 [63.0, 155]	88.0 [63.0, 103]
Inhibition
Mean (SD)	22.8 (6.00)	22.9 (4.90)
Median [Min, Max]	23.0 [16.0, 46.0]	22.0 [16.0, 30.0]
Shift
Mean (SD)	13.2 (3.11)	13.5 (2.67)
Median [Min, Max]	12.0 [10.0, 24.0]	13.0 [10.0, 17.0]
Emotional Control
Mean (SD)	15.1 (3.69)	14.7 (2.96)
Median [Min, Max]	14.5 [10.0, 24.0]	14.0 [10.0, 19.0]
Working Memory
Mean (SD)	22.9 (6.72)	21.6 (4.27)
Median [Min, Max]	21.0 [17.0, 48.0]	21.0 [17.0, 31.0]
Plan/Organize
Mean (SD)	14.4 (3.44)	13.1 (2.42)
Median [Min, Max]	14.0 [10.0, 23.0]	13.0 [10.0, 17.0]

t.test(BRIEF_early_AgeApp$GEC_RawScore~BRIEF_early_AgeApp$Language_Modality)

## 
##  Welch Two Sample t-test
## 
## data:  BRIEF_early_AgeApp$GEC_RawScore by BRIEF_early_AgeApp$Language_Modality
## t = 0.57581, df = 34.041, p-value = 0.5685
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -6.541513 11.714240
## sample estimates:
## mean in group English     mean in group ASL 
##              88.38636              85.80000

t.test(BRIEF_early_AgeApp$Inhibit_RawScore~BRIEF_early_AgeApp$Language_Modality)

## 
##  Welch Two Sample t-test
## 
## data:  BRIEF_early_AgeApp$Inhibit_RawScore by BRIEF_early_AgeApp$Language_Modality
## t = -0.016572, df = 29.461, p-value = 0.9869
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -3.202462  3.150947
## sample estimates:
## mean in group English     mean in group ASL 
##              22.84091              22.86667

t.test(BRIEF_early_AgeApp$Shift_RawScore~BRIEF_early_AgeApp$Language_Modality)

## 
##  Welch Two Sample t-test
## 
## data:  BRIEF_early_AgeApp$Shift_RawScore by BRIEF_early_AgeApp$Language_Modality
## t = -0.36901, df = 28.005, p-value = 0.7149
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -2.014922  1.399771
## sample estimates:
## mean in group English     mean in group ASL 
##              13.15909              13.46667

t.test(BRIEF_early_AgeApp$Emotional.Control_RawScore~BRIEF_early_AgeApp$Language_Modality)

## 
##  Welch Two Sample t-test
## 
## data:  BRIEF_early_AgeApp$Emotional.Control_RawScore by BRIEF_early_AgeApp$Language_Modality
## t = 0.40191, df = 30.011, p-value = 0.6906
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -1.552126  2.312732
## sample estimates:
## mean in group English     mean in group ASL 
##              15.11364              14.73333

t.test(BRIEF_early_AgeApp$Working.Memory_RawScore~BRIEF_early_AgeApp$Language_Modality)

## 
##  Welch Two Sample t-test
## 
## data:  BRIEF_early_AgeApp$Working.Memory_RawScore by BRIEF_early_AgeApp$Language_Modality
## t = 0.84371, df = 38.617, p-value = 0.404
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -1.766731  4.294004
## sample estimates:
## mean in group English     mean in group ASL 
##              22.86364              21.60000

t.test(BRIEF_early_AgeApp$Plan.Organize_RawScore~BRIEF_early_AgeApp$Language_Modality)

## 
##  Welch Two Sample t-test
## 
## data:  BRIEF_early_AgeApp$Plan.Organize_RawScore by BRIEF_early_AgeApp$Language_Modality
## t = 1.5727, df = 34.639, p-value = 0.1249
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.3716748  2.9231899
## sample estimates:
## mean in group English     mean in group ASL 
##              14.40909              13.13333

Supplementary Materials

Table 1: Comparing Global Executive Composite Models

stargazer(overall_all_base, overall_all_AoLE, overall_all_AoLE_AoAE, overall_all_AoLE_LoAE, overall_all_AoLE_Modality,overall_all_AoLE_HearStat, type= "html", title = "Linear Regression Results", align=TRUE, dep.var.labels=c("Global Executive Composite Raw Scores"), covariate.labels=c("Socioeconomic Status (SES)", "Sex (Male)", "Age (Months)", "Age of Exposure to Language (Months)", "Age of Auditory Exposure (Months)", "Length of Auditory Experience (Months)", "Language Modality (ASL)", "Hearing Status (Deaf)", "Hearing Status (HoH)"), out="BRIEF_supp_table1.html")

Table 2: Comparing Inhibition Models

stargazer(inhibition_all_base, inhibition_all_AoLE, inhibition_all_AoLE_AoAE, inhibition_all_AoLE_LoAE, inhibition_all_AoLE_Modality, inhibition_all_AoLE_HearStat, type= "html", title = "Linear Regression Results", align=TRUE, dep.var.labels=c("Inhibition Raw Scores"), covariate.labels=c("Socioeconomic Status (SES)", "Sex (Male)", "Age (Months)", "Age of Exposure to Language (Months)", "Age of Auditory Exposure (Months)", "Length of Auditory Experience (Months)", "Language Modality (ASL)", "Hearing Status (Deaf)", "Hearing Status (HoH)"), out="BRIEF_supp_table2.html")

Table 3: Comparing Shift Models.

stargazer(shift_all_base, shift_all_AoLE, shift_all_AoLE_AoAE, shift_all_AoLE_LoAE, shift_all_AoLE_Modality, shift_all_AoLE_HearStat, type= "html", title = "Linear Regression Results", align=TRUE, dep.var.labels=c("Shift Raw Scores"), covariate.labels=c("Socioeconomic Status (SES)", "Sex (Male)", "Age (Months)", "Age of Exposure to Language (Months)", "Age of Auditory Exposure (Months)", "Length of Auditory Experience (Months)", "Language Modality (ASL)", "Hearing Status (Deaf)", "Hearing Status (HoH)"), out="BRIEF_supp_table3.html")

Table 4: Comparing Emotional Control Models

stargazer(emotctrl_all_base, emotctrl_all_AoLE, emotctrl_all_AoLE_AoAE, emotctrl_all_AoLE_LoAE, emotctrl_all_AoLE_Modality, emotctrl_all_AoLE_HearStat, type= "html", title = "Linear Regression Results", align=TRUE, dep.var.labels=c("Emotional Control Raw Scores"), covariate.labels=c("Socioeconomic Status (SES)", "Sex (Male)", "Age (Months)", "Age of Exposure to Language (Months)", "Age of Auditory Exposure (Months)", "Length of Auditory Experience (Months)", "Language Modality (ASL)", "Hearing Status (Deaf)", "Hearing Status (HoH)"), out="BRIEF_supp_table4.html")

Table 5: Comparing Working Memory Models

stargazer(wm_all_base, wm_all_AoLE, wm_all_AoLE_AoAE, wm_all_AoLE_LoAE, wm_all_AoLE_Modality,wm_all_AoLE_HearStat, type= "html", title = "Linear Regression Results", align=TRUE, dep.var.labels=c("Working Memory Raw Scores"), covariate.labels=c("Socioeconomic Status (SES)", "Sex (Male)", "Age (Months)", "Age of Exposure to Language (Months)", "Age of Auditory Exposure (Months)", "Length of Auditory Experience (Months)", "Language Modality (ASL)", "Hearing Status (Deaf)", "Hearing Status (HoH)"), out="BRIEF_supp_table5.html")

Table 6: Comparing Planning/Organization Models

stargazer(plan_all_base, plan_all_AoLE, plan_all_AoLE_AoAE, plan_all_AoLE_LoAE, plan_all_AoLE_Modality, plan_all_AoLE_HearStat, type= "html", title = "Linear Regression Results", align=TRUE, dep.var.labels=c("Planning/Organization Raw Scores"), covariate.labels=c("Socioeconomic Status (SES)", "Sex (Male)", "Age (Months)", "Age of Exposure to Language (Months)", "Age of Auditory Exposure (Months)", "Length of Auditory Experience (Months)", "Language Modality (ASL)", "Hearing Status (Deaf)", "Hearing Status (HoH)"), out="BRIEF_supp_table6.html")

Table 7: Demographic information for the subset of participants who were in the appropriate age range for the BRIEF-P

table1::label(BRIEF_AgeApp$AgeMonths) <- "Age (Months)"
table1::label(BRIEF_AgeApp$Sex) <- "Sex"
table1::label(BRIEF_AgeApp$Race_recoded) <- "Race"
table1::label(BRIEF_AgeApp$Ethnicity_recoded) <- "Ethnicity"
table1::label(BRIEF_AgeApp$SES..3.66.) <- "SES"
 
table1::table1(~AgeMonths + Sex + Race_recoded + Ethnicity_recoded + SES..3.66. | LanguageGroup, data = BRIEF_AgeApp, overall=F)

	Typically Hearing (N=44)	Early ASL (N=15)	Later English (N=29)	Later ASL (N=14)
Age (Months)
Mean (SD)	53.7 (9.80)	53.3 (8.70)	55.2 (10.0)	56.3 (11.0)
Median [Min, Max]	54.0 [37.0, 71.0]	54.0 [41.0, 70.0]	58.0 [37.0, 71.0]	57.0 [37.0, 71.0]
Sex
Female	23 (52.3%)	7 (46.7%)	15 (51.7%)	7 (50.0%)
Male	21 (47.7%)	8 (53.3%)	14 (48.3%)	7 (50.0%)
Race
Asian	0 (0%)	0 (0%)	1 (3.4%)	2 (14.3%)
Black or African American	0 (0%)	0 (0%)	1 (3.4%)	0 (0%)
White	41 (93.2%)	14 (93.3%)	22 (75.9%)	11 (78.6%)
More than one	3 (6.8%)	0 (0%)	3 (10.3%)	1 (7.1%)
Other/Missing	0 (0%)	1 (6.7%)	2 (6.9%)	0 (0%)
Ethnicity
Hispanic	3 (6.8%)	0 (0%)	3 (10.3%)	1 (7.1%)
Non-Hispanic	40 (90.9%)	9 (60.0%)	22 (75.9%)	11 (78.6%)
Prefer not to answer	0 (0%)	1 (6.7%)	0 (0%)	0 (0%)
Missing	1 (2.3%)	5 (33.3%)	4 (13.8%)	2 (14.3%)
SES
Mean (SD)	55.5 (8.83)	46.3 (16.5)	49.2 (13.7)	50.6 (9.57)
Median [Min, Max]	56.0 [21.5, 66.0]	55.0 [19.0, 66.0]	53.0 [3.00, 66.0]	53.0 [25.5, 62.0]

Table 8. Relative Risk of Clinically-Elevated (T-score ≥ 60) Executive Functioning Deficits in DHH Participants

Risk Ratios (Relative Risk) and 95% confidence intervals for Global Executive Composite T-scores > 60

BRIEF_AgeApp_GEC_over60 <- BRIEF_AgeApp %>% 
    group_by(LanguageGroup) %>% 
    summarise("Global Executive Composite T-score < 60 (n)" = sum(GEC_Tscore < 60), "Global Executive Composite T-score > 60 (n)" = sum(GEC_Tscore >= 60))

## `summarise()` ungrouping output (override with `.groups` argument)

BRIEF_AgeApp_GEC_over60 %>%
    kable() %>%  
    kable_styling(bootstrap_options = "striped")

LanguageGroup	Global Executive Composite T-score < 60 (n)	Global Executive Composite T-score > 60 (n)
Typically Hearing	39	5
Early ASL	15	0
Later English	21	8
Later ASL	10	4

GEC_60rr <- matrix(c(39,5,18,3, 21, 8, 11, 5), 4, 2, byrow=TRUE)
dimnames(GEC_60rr) <- list("Group" = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"), "GEC T-score" = c("Not Elevated", "Elevated"))
riskratio.small(GEC_60rr)

## $data
##                    GEC T-score
## Group               Not Elevated Elevated Total
##   Typically Hearing           39        5    44
##   Early ASL                   18        3    21
##   Later English               21        8    29
##   Later ASL                   11        5    16
##   Total                       89       21   110
## 
## $measure
##                    risk ratio with 95% C.I.
## Group               estimate     lower    upper
##   Typically Hearing 1.000000        NA       NA
##   Early ASL         1.071429 0.2823442 4.065814
##   Later English     2.068966 0.7503603 5.704751
##   Later ASL         2.343750 0.7804552 7.038410
## 
## $p.value
##                    two-sided
## Group               midp.exact fisher.exact chi.square
##   Typically Hearing         NA           NA         NA
##   Early ASL         0.73734103    0.7063030 0.73735891
##   Later English     0.09251239    0.1170560 0.07626226
##   Later ASL         0.09611194    0.1124258 0.06757726
## 
## $correction
## [1] FALSE
## 
## attr(,"method")
## [1] "small sample-adjusted UMLE & normal approx (Wald) CI"

#DHH groups not significantly more likely to have elevated GEC T-scores relative to TH group, but the two later groups are close

Inhibit T-scores > 60 Risk Ratios and 95% Confidence Intervals

BRIEF_AgeApp_Inhibit_over60 <- BRIEF_AgeApp %>% 
    group_by(LanguageGroup) %>% 
    summarise("Inhibit T-score < 60 (n)" = sum(Inhibit_Tscore < 60), "Inhibit T-score > 60 (n)" = sum(Inhibit_Tscore >= 60))

## `summarise()` ungrouping output (override with `.groups` argument)

BRIEF_AgeApp_Inhibit_over60 %>%
    kable() %>%  
    kable_styling(bootstrap_options = "striped")

LanguageGroup	Inhibit T-score < 60 (n)	Inhibit T-score > 60 (n)
Typically Hearing	39	5
Early ASL	13	2
Later English	25	4
Later ASL	9	5

Inhibit_60rr <- matrix(c(39,5,17,4, 25, 4, 11, 5), 4, 2, byrow=TRUE)
dimnames(Inhibit_60rr) <- list("Group" = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"), "Inhibit T-score" = c("Not Elevated", "Elevated"))
riskratio.small(Inhibit_60rr)

## $data
##                    Inhibit T-score
## Group               Not Elevated Elevated Total
##   Typically Hearing           39        5    44
##   Early ASL                   17        4    21
##   Later English               25        4    29
##   Later ASL                   11        5    16
##   Total                       92       18   110
## 
## $measure
##                    risk ratio with 95% C.I.
## Group               estimate     lower    upper
##   Typically Hearing 1.000000        NA       NA
##   Early ASL         1.428571 0.4269974 4.779458
##   Later English     1.034483 0.3028657 3.533430
##   Later ASL         2.343750 0.7804552 7.038410
## 
## $p.value
##                    two-sided
## Group               midp.exact fisher.exact chi.square
##   Typically Hearing         NA           NA         NA
##   Early ASL         0.42858263    0.4545627 0.40157575
##   Later English     0.76192336    1.0000000 0.75736046
##   Later ASL         0.09611194    0.1124258 0.06757726
## 
## $correction
## [1] FALSE
## 
## attr(,"method")
## [1] "small sample-adjusted UMLE & normal approx (Wald) CI"

#DHH groups *not* significantly more likely to have elevated Inhibit T-scores relative to TH group, but the Later ASL group close

Shift T-scores > 60 Risk Ratios and 95% Confidence Intervals

BRIEF_AgeApp_Shift_over60 <- BRIEF_AgeApp %>% 
    group_by(LanguageGroup) %>% 
    summarise("Shift T-score < 60 (n)" = sum(Shift_Tscore < 60), "Shift T-score > 60 (n)" = sum(Shift_Tscore >= 60))

## `summarise()` ungrouping output (override with `.groups` argument)

BRIEF_AgeApp_Shift_over60 %>%
    kable() %>%  
    kable_styling(bootstrap_options = "striped")

LanguageGroup	Shift T-score < 60 (n)	Shift T-score > 60 (n)
Typically Hearing	42	2
Early ASL	15	0
Later English	26	3
Later ASL	11	3

Shift_60rr <- matrix(c(42,2,19,2, 26, 2, 12, 4), 4, 2, byrow=TRUE)
dimnames(Shift_60rr) <- list("Group" = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"), "Shift T-score" = c("Not Elevated", "Elevated"))
riskratio.small(Shift_60rr)

## $data
##                    Shift T-score
## Group               Not Elevated Elevated Total
##   Typically Hearing           42        2    44
##   Early ASL                   19        2    21
##   Later English               26        2    28
##   Later ASL                   12        4    16
##   Total                       99       10   109
## 
## $measure
##                    risk ratio with 95% C.I.
## Group               estimate     lower     upper
##   Typically Hearing 1.000000        NA        NA
##   Early ASL         1.428571 0.2158668  9.454055
##   Later English     1.071429 0.1599591  7.176581
##   Later ASL         3.750000 0.7586056 18.537301
## 
## $p.value
##                    two-sided
## Group               midp.exact fisher.exact chi.square
##   Typically Hearing         NA           NA         NA
##   Early ASL         0.48397436   0.58920596 0.43476804
##   Later English     0.66760563   0.63954549 0.63902828
##   Later ASL         0.04238826   0.03838937 0.01951748
## 
## $correction
## [1] FALSE
## 
## attr(,"method")
## [1] "small sample-adjusted UMLE & normal approx (Wald) CI"

#Later ASL group significantly more likely to have elevated Shift T-scores relative to TH group (3.75 times more likely) according to p-values (even though risk ratio CI does include 1)

Emotional Control T-scores > 60 Risk Ratios and 95% Confidence Intervals

BRIEF_AgeApp_EmoCtrl_over60 <- BRIEF_AgeApp %>% 
    group_by(LanguageGroup) %>% 
    summarise("Emotional Control T-score < 60 (n)" = sum(Emotional.Control_Tscore < 60), "Emotional Control T-score > 60 (n)" = sum(Emotional.Control_Tscore >= 60))

## `summarise()` ungrouping output (override with `.groups` argument)

BRIEF_AgeApp_EmoCtrl_over60 %>%
    kable() %>%  
    kable_styling(bootstrap_options = "striped")

LanguageGroup	Emotional Control T-score < 60 (n)	Emotional Control T-score > 60 (n)
Typically Hearing	33	11
Early ASL	15	0
Later English	27	2
Later ASL	12	2

EmoCtrl_60rr <- matrix(c(33,11, 19,2, 27,2, 13,3), 4, 2, byrow=TRUE)
dimnames(EmoCtrl_60rr) <- list("Group" = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"), "Emotional Control T-score" = c("Not Elevated", "Elevated"))
riskratio.small(EmoCtrl_60rr)

## $data
##                    Emotional Control T-score
## Group               Not Elevated Elevated Total
##   Typically Hearing           33       11    44
##   Early ASL                   19        2    21
##   Later English               27        2    29
##   Later ASL                   13        3    16
##   Total                       92       18   110
## 
## $measure
##                    risk ratio with 95% C.I.
## Group                estimate      lower    upper
##   Typically Hearing 1.0000000         NA       NA
##   Early ASL         0.3571429 0.08683599 1.468873
##   Later English     0.2586207 0.06177609 1.082695
##   Later ASL         0.7031250 0.22460549 2.201125
## 
## $p.value
##                    two-sided
## Group               midp.exact fisher.exact chi.square
##   Typically Hearing         NA           NA         NA
##   Early ASL         0.15834537   0.19439161 0.14463160
##   Later English     0.05147564   0.06302486 0.04789045
##   Later ASL         0.64844160   0.73965677 0.61273516
## 
## $correction
## [1] FALSE
## 
## attr(,"method")
## [1] "small sample-adjusted UMLE & normal approx (Wald) CI"

#Later English borderline *less* likely than TH to have elevated Emotional Control T-scores (but there do seem to be an unexpectedly high number of TH kids in the "elevated" score range, and Later English group only on average 4 months older than TH kids)

Working Memory T-scores > 60 Risk Ratios and 95% Confidence Intervals

BRIEF_AgeApp_WorkMem_over60 <- BRIEF_AgeApp %>% 
    group_by(LanguageGroup) %>% 
    summarise("Working Memory T-score < 60 (n)" = sum(Working.Memory_Tscore < 60), "Working Memory T-score > 60 (n)" = sum(Working.Memory_Tscore >= 60))

## `summarise()` ungrouping output (override with `.groups` argument)

BRIEF_AgeApp_WorkMem_over60 %>%
    kable() %>%  
    kable_styling(bootstrap_options = "striped")

LanguageGroup	Working Memory T-score < 60 (n)	Working Memory T-score > 60 (n)
Typically Hearing	38	6
Early ASL	13	2
Later English	21	8
Later ASL	8	6

WorkMem_60rr <- matrix(c(38,6,17,4,21,8,9,7), 4, 2, byrow=TRUE)
dimnames(WorkMem_60rr) <- list("Group" = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"), "Working Memory T-score" = c("Not Elevated", "Elevated"))
riskratio.small(WorkMem_60rr)

## $data
##                    Working Memory T-score
## Group               Not Elevated Elevated Total
##   Typically Hearing           38        6    44
##   Early ASL                   17        4    21
##   Later English               21        8    29
##   Later ASL                    9        7    16
##   Total                       85       25   110
## 
## $measure
##                    risk ratio with 95% C.I.
## Group               estimate     lower    upper
##   Typically Hearing 1.000000        NA       NA
##   Early ASL         1.224490 0.3863952 3.880419
##   Later English     1.773399 0.6865126 4.581043
##   Later ASL         2.812500 1.1116392 7.115759
## 
## $p.value
##                    two-sided
## Group               midp.exact fisher.exact chi.square
##   Typically Hearing         NA           NA         NA
##   Early ASL         0.58471003   0.71529371 0.57175432
##   Later English     0.15825023   0.22343601 0.13850571
##   Later ASL         0.02168355   0.02870431 0.01228576
## 
## $correction
## [1] FALSE
## 
## attr(,"method")
## [1] "small sample-adjusted UMLE & normal approx (Wald) CI"

#Later ASL significantly more likely to have elevated Working Memory T-scores relative to TH group

Plan/Organize T-scores > 60 Risk Ratios and 95% Confidence Intervals

BRIEF_AgeApp_PlanOrg_over60 <- BRIEF_AgeApp %>% 
    group_by(LanguageGroup) %>% 
    summarise("Plan/Organize T-score < 60 (n)" = sum(Plan.Organize_Tscore < 60), "Plan/Organize T-score > 60 (n)" = sum(Plan.Organize_Tscore >= 60))

## `summarise()` ungrouping output (override with `.groups` argument)

BRIEF_AgeApp_PlanOrg_over60 %>%
    kable() %>%  
    kable_styling(bootstrap_options = "striped")

LanguageGroup	Plan/Organize T-score < 60 (n)	Plan/Organize T-score > 60 (n)
Typically Hearing	37	7
Early ASL	14	1
Later English	19	10
Later ASL	10	4

PlanOrg_60rr <- matrix(c(37,7,18,3, 19, 10, 11, 5), 4, 2, byrow=TRUE)
dimnames(PlanOrg_60rr) <- list("Group" = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"), "Plan/Organize T-score" = c("Not Elevated", "Elevated"))
riskratio.small(PlanOrg_60rr)

## $data
##                    Plan/Organize T-score
## Group               Not Elevated Elevated Total
##   Typically Hearing           37        7    44
##   Early ASL                   18        3    21
##   Later English               19       10    29
##   Later ASL                   11        5    16
##   Total                       85       25   110
## 
## $measure
##                    risk ratio with 95% C.I.
## Group                estimate     lower    upper
##   Typically Hearing 1.0000000        NA       NA
##   Early ASL         0.8035714 0.2305467 2.800851
##   Later English     1.9396552 0.8336220 4.513152
##   Later ASL         1.7578125 0.6500190 4.753561
## 
## $p.value
##                    two-sided
## Group               midp.exact fisher.exact chi.square
##   Typically Hearing         NA           NA         NA
##   Early ASL         0.89457715   1.00000000 0.86529254
##   Later English     0.07860199   0.09078038 0.06617443
##   Later ASL         0.22088878   0.27301329 0.18894147
## 
## $correction
## [1] FALSE
## 
## attr(,"method")
## [1] "small sample-adjusted UMLE & normal approx (Wald) CI"

#DHH groups *not* significantly more likely to have elevated Inhibit T-scores relative to TH group